# Active Learning for Video Classification with Frame Level Queries
††thanks: This research was supported in part by the National Science
Foundation under Grant Number: 2143424

Debanjan Goswami  Department of Computer Science
Florida State University
Shayok Chakraborty  Department of Computer Science
Florida State University

###### Abstract

Deep learning algorithms have pushed the boundaries of computer vision
research and have depicted commendable performance in a variety of
applications. However, training a robust deep neural network necessitates a
large amount of labeled training data, acquiring which involves significant
time and human effort. This problem is even more serious for an application
like video classification, where a human annotator has to watch an entire
video end-to-end to furnish a label. Active learning algorithms automatically
identify the most informative samples from large amounts of unlabeled data;
this tremendously reduces the human annotation effort in inducing a machine
learning model, as only the few samples that are identified by the algorithm,
need to be labeled manually. In this paper, we propose a novel active learning
framework for video classification, with the goal of further reducing the
labeling onus on the human annotators. Our framework identifies a batch of
exemplar videos, together with a set of informative frames for each video; the
human annotator needs to merely review the frames and provide a label for each
video. This involves much less manual work than watching the complete video to
come up with a label. We formulate a criterion based on uncertainty and
diversity to identify the informative videos and exploit representative
sampling techniques to extract a set of exemplar frames from each video. To
the best of our knowledge, this is the first research effort to develop an
active learning framework for video classification, where the annotators need
to inspect only a few frames to produce a label, rather than watching the end-
to-end video. Our extensive empirical analyses corroborate the potential of
our method to substantially reduce human annotation effort in applications
like video classification, where annotating a single data instance can be
extremely tedious.

###### Index Terms:

active learning, video classification, deep learning

##  I Introduction

With the widespread deployment of modern sensors and cameras, images and
videos have become ubiquitous. This has encouraged the development of video
classification algorithms to analyze their semantic content for various
applications, such as search, summarization, security and surveillance among
others. Deep neural networks (CNN and LSTM architectures) have depicted
commendable performance in this field [1]. Common methods include obtaining
global video-level descriptors using CNN architectures [2], processing videos
at two spatial resolutions: a low-resolution context stream and a high-
resolution fovea stream [3], fusion technique to integrate data
representations at the frame level and video level [4] among others. However,
for all these models to work reliably, a large amount of labeled training data
is essential, gathering which is an expensive process in terms of time, labor
and human expertise. Thus, an algorithm to reduce the human labeling effort is
of immense importance in video classification applications.

(a) Conventional AL query for video classification

(b) Proposed active query and annotation mechanism

Figure 1: (a) Conventional AL query for video classification, where the human
annotator has to watch a video end-to-end to provide a label. (b) Proposed
active query and annotation mechanism where we actively select a batch of
videos, together with a subset of frames from each video; the human annotator
merely needs to inspect the frames and provide a label for the video.

Active Learning (AL) is a machine learning paradigm, where the goal is to
automatically identify the salient and exemplar samples from large amounts of
redundant data [5]. This tremendously reduces the human annotation effort in
inducing a machine learning model, since the human expert only has to label
the samples queried by the algorithm. Further, since the model gets trained on
the exemplar samples from the data population, it typically depicts better
generalization performance than a model where the training data is selected at
random. This is an extremely relevant paradigm in today’s world, where an
enormous amount of digital data is being generated, but there is a serious
dearth of human labor to annotate the data to induce learning models. AL has
been successfully used in a variety of applications, including computer vision
[6], text analysis [7], computational biology [8] and medical diagnosis [9]
among others. Active learning is particularly relevant in the context of deep
learning, in order to reduce human annotation effort in training the data-
hungry deep neural networks [10].

Designing an AL algorithm for a video classification application entails the
human annotator to meticulously watch each queried video end-to-end in order
to furnish a label 111we use the terms annotators, oracles, labelers and users
synonymously in this paper. This is an extremely time-consuming and laborious
process; the annotators may get bored and fatigued quickly and lose interest
in the task. This necessitates specialized and more user-friendly query and
annotation mechanisms, to utilize the available human labor more efficiently.
In this paper, we propose a novel active learning algorithm to address this
challenging and practical problem. Our algorithm identifies a batch of
informative videos, together with a set of exemplar frames from each; the
human annotator merely has to review the queried frames and furnish a label
for each video. This is illustrated in Figure 1. Providing such feedback is
significantly less time-consuming and burdensome than watching an end-to-end
video. We formulate an optimization problem based on an uncertainty and
diversity based criterion to identify a batch of informative videos, and
exploit representative sampling techniques to select a subset of exemplar
frames from each. To our knowledge, this is the first active learning
framework for video classification which poses label queries based on a set of
exemplar frames, rather than the complete video. We hope this research will
motivate the development of AL algorithms with other novel annotation
mechanisms, with the goal of further reducing the labeling burden on human
oracles in a video classification application.

The rest of the paper is organized as follows: we present a survey of related
research in Section II, our active sampling framework is detailed in Section
III, the results of our empirical studies are presented in Section IV, and we
conclude with discussions in Section V.

##  II Related Work

In this section, we present an overview of active learning in general,
followed by a survey of AL for video classification.

Active Learning: AL has received significant research attention in the machine
learning community. Uncertainty sampling is by far the most common strategy
for active learning, where unlabeled samples with highest classification
uncertainties are queried for their labels. The uncertainty of an unlabeled
sample can be computed by its entropy [11], its distance from the separating
hyperplane in the feature space for SVM classifiers [7], the disagreement
among a committee of classifiers regarding the label of the sample [12], the
expected error reduction of the future learner [13] and so on. Submodular
optimization techniques have also been exploited for active data sampling [14,
15].

The growing success and popularity of deep learning has motivated research in
the field of deep active learning (DAL), where the goal is to select
informative unlabeled samples to efficiently train a deep neural network [10].
A task agnostic AL framework was proposed by Yoo and Kweon [6] that
incorporated a loss prediction module in the network architecture, to predict
the loss value of an unlabeled sample and query samples accordingly. A DAL
framework based on core-set selection was proposed by Sener and Savarese [16],
which selected a batch of samples, such that the deep model trained on the
selected samples depicts similar performance to that trained on the whole
dataset. DAL has also been studied in conjunction with neural architecture
search [17], which queries samples for labeling and simultaneously searches
for the best neural architectures on-the-fly. A novel training loss function
for DAL was proposed by Shui et al., where active sample selection and traning
the network parameters were achieved through alternating optimization [18].
Deep active learning techniques based on adversarial training have depicted
particularly impressive performance [19, 20, 21, 22]. Active learning has also
been studied in conjunction with other learning paradigms such as transfer
learning [23], reinforcement learning [24] etc. Moreover, the idea of
identifying an informative set of samples for human inspection has been
extended to other problem domains, such as matrix completion [25], video
summarization [26] and feature selection [27] among others.

Recently, there have been efforts to design AL systems with novel query and
annotation mechanisms, with the goal of further reducing the labeling burden
on human annotators. Joshi et al. [28] proposed a binary query mechanism which
queried an unlabeled sample together with a potential class label and the user
had to provide the binary answer as to whether the queried unlabeled sample
belonged to the selected class or not. Along similar lines, Biswas and Jacobs
proposed an AL algorithm for clustering, which queried a pair of samples and
the oracles needed to specify whether or not the samples in a pair correspond
to the same cluster [29]. Xiong et al. [30] proposed a triplet query framework
to learn approximate distance metrics for a nearest neighbor classifier; the
algorithm queried unlabeled data triplets $(x_{i},x_{j},x_{k})$ and posed the
question whether instance $x_{i}$ was more similar to $x_{j}$ than to $x_{k}$.
Qian et al. [31] proposed an active learning algorithm where the query
strategy was to place an ordering (or partial ordering) on the similarity of
the neighbors of the selected unlabeled sample, rather than querying its
actual label.

Active Learning for Video Classification: While AL has been extensively
studied for image recognition [6, 32, 22, 33], it is much less explored for
video classification. Similar to image classification, uncertainty sampling
(using metrics like entropy, error reduction) is a popular AL query strategy
for video recognition [34, 35]. Yan et al. [36] proposed a multi-class AL
framework for video classification using expected error reduction. Since the
estimation of the posterior probability distribution $P(y|x)$ may be
unreliable due to the lack of sufficient training data, simple heuristics were
also proposed to simplify the sample selection strategies. Another approach
was developed in the context of SVMs, which queried a set of samples which can
produce the maximum expected reduction in the SVM objective [37, 38]. Bandla
and Grauman [39] used AL to train an action detector for videos which selected
the video which was expected to maximally reduce the entropy among all
unlabeled videos. The core idea was to use the current trained detector to
extract relevant portions in the video where the action of interest occurs, so
that the video segment outside the interval does not introduce noise in the
entropy computation. However, this method is specifically designed to actively
learn an action detector from videos. Active contrastive learning has also
been explored for learning audio-visual representations from unlabeled videos
[40].

All these methods require the human annotator to watch an unlabeled video end-
to-end in order to provide a label, which may be extremely time-consuming and
arduous. In contrast, our framework identifies a subset of exemplar frames,
and the human labeler has to label a video by merely reviewing the frames,
which is a much more efficient annotation strategy. Our method is applicable
to any type of videos and does not make any assumptions about the contents of
the video. Other related efforts include AL for video tracking [41], video
description [42], video recommendation [43] and video segmentation [44].
However, these methods attempt to solve a different problem than video
classification, which is the focus of this paper. We now describe our
framework.

##  III Proposed Framework

Consider an active learning problem for video classification, where we are
given a labeled training set $L$ and an unlabeled set $U$, with $|L|\ll|U|$.
Each data sample $x$ in $L$ and $U$ is a video. Let $w$ be the deep neural
network trained on $L$ and $C$ be the number of classes in the data. Our
objective is two-fold: $(i)$ select a batch $B$ containing $b$ unlabeled
videos so that the model trained on $L\cup B$ has maximum generalization
capability; $(ii)$ however, we are not allowed to show an entire video to a
human annotator and ask for its label; we are required to select a subset of
$k$ exemplar frames from each queried video, so that only those can be shown
to an annotator for labeling the video.

Both these objectives are critical in improving the generalization capability
of the deep model. The first objective ensures that the salient videos are
selected from the unlabeled set for active query. The second objective ensures
that the most representative frames are selected from each video for query.
This is important, as otherwise, the annotator may not be confident enough to
provide a label or may provide an incorrect label, both of which will result
in a wastage of query budget and degrade the performance of the model. In the
following sections, we discuss our active sampling strategies for sampling
videos and frames.

###  III-A Active Video Sampling

We quantified the utility of a batch of $b$ videos and selected a batch
furnishing the maximal utility. The informativeness and diversity metrics were
used to compute the utility of a batch of videos in this research. An active
learning framework driven by these conditions ensures that the video samples
in the batch augment useful knowledge to the underlying deep neural network,
and there is high diversity (minimum redundancy) of information among the
samples in the batch. These conditions have been used in previous active
learning research [45].

Computing informativeness: The informativeness of an unlabeled video sample
$x_{i}$ was computed as the uncertainty of the deep model $w$ in predicting a
label for $x_{i}$. The Shannon’s entropy was used to compute the prediction
uncertainty:

$e(x_{i})=-\sum_{y=1}^{C}P(y|x_{i},w)\log P(y|x_{i},w)$ (1)

Computing diversity: We computed a diversity matrix $R\in\Re^{|U|\times|U|}$
where $R(i,j)$ denotes the diversity between videos $x_{i}$ and $x_{j}$ in the
unlabeled set. We used the kernelized distance on the deep feature
representations to compute the diversity between a pair of videos in this
research:

$R(i,j)=K(x_{i},x_{j})$ (2)

where $K=(.,.)$ denotes the distance in the Reproducing Kernel Hilbert Space
(RKHS) [46].

####  III-A1 Active Video Selection

By definition, all the entries in $e$ and $R$ are non-negative, that is,
$e_{i}\geq 0$ and $R(i,j)\geq 0,\forall i,j$. Given $e$ and $R$, our objective
is to select a batch of videos with high uncertainties (given by the entries
in $e$) and high mutual diversity (given by the entries in $R$). We define a
binary selection vector $z\in\Re^{|U|\times 1}$ where $z_{i}$ denotes whether
the unlabeled video $x_{i}$ will be selected in the batch $(z_{i}=1)$ or not
$(z_{i}=0)$. Our batch selection task (with batch size $b$) can thus be posed
as the following NP-hard integer quadratic programming (IQP) problem:

$\max_{z}e^{T}z+\mu z^{T}Rz$ $\text{s.t.}\hskip
7.22743ptz_{i}\in\\{0,1\\},\forall i\hskip 7.22743pt\text{and}\hskip
7.22743pt\sum_{i=1}^{|U|}z_{i}=b$ (3)

where $\mu$ is a weight parameter governing the relative importance of the two
terms. The binary integer constraints on $z$ allow us to combine $e$ and $R$
into a single matrix $Q\in\Re^{|U|\times|U|}$ and express the optimization
problem as follows:

$\max_{z}z^{T}Qz$ $\text{s.t.}\hskip 7.22743ptz_{i}\in\\{0,1\\},\forall
i\hskip 7.22743pt\text{and}\hskip 7.22743pt\sum_{i=1}^{|U|}z_{i}=b$ (4)

where the matrix $Q$ is constructed as follows:

$Q(i,j)=\begin{cases}\mu R(i,j),&\text{if}\hskip 2.168pti\neq j\\\
e(i),&\text{if}\hskip 2.168pti=j\end{cases}$ (5)

The binary integer constraints on the variable $z$ make the IQP in Equation
(4) NP-hard. We used the Iterative Truncated Power algorithm [47] to solve
this optimization problem.

####  III-A2 The Iterative Truncated Power Algorithm

This algorithm was originally proposed in the context of the sparse eigenvalue
and the densest $k$-subgraph problems. It attempts to solve an optimization
problem similar to that in Equation (4). The algorithm starts with an initial
solution $z_{0}$ and then generates a sequence of solutions
$z_{1},z_{2},\ldots$. The solution $z_{t}$ at iteration $t$ is obtained by
multiplying the solution $z_{t-1}$ at iteration $(t-1)$ by the matrix $Q$ and
then truncating all the entries to $0$, except the $b$ largest entries. The
process is repeated until convergence. The algorithm is guaranteed to converge
monotonically for a positive semi-definite (psd) matrix $Q$. When the matrix
$Q$ is not psd, the algorithm can be run on the shifted quadratic function
(with a positive scalar added to the diagonal elements) to guarantee a
monotonic convergence [47]. The algorithm is computationally efficient and
converges fast. It benefits from a good starting point. In our empirical
studies, the initial solution $z_{0}$ was taken as the indicator vector
corresponding to the $b$ largest column sums of the matrix $Q$, as it produced
competitive results in our preliminary experiments. The pseudo-code for our
active video sampling algorithm is presented in Algorithm 1.

Algorithm 1 The Proposed Active Video Sampling Algorithm

0: Training set $L$, Unlabeled set $U$, batch size $b$ and weight parameter
$\mu$

1: Train a deep neural network $w$ on the training set $L$

2: Compute the entropy vector $e$ (Equation (1)) and the diversity matrix $R$
(Equation (2))

3: Compute the matrix $Q$, as described in Equation (5)

4: Derive the initial solution $z_{0}$ as the indicator vector containing the
$b$ largest column sums of the matrix $Q$

5: t = 1

6: repeat

7: Compute $\widehat{z_{t}}=Q.z_{t-1}$

8: Identify $F_{t}$ as the index set of $\widehat{z_{t}}$ with top $b$ values

9: Set $z_{t}$ to be $1$ on the index set $F_{t}$ and $0$ otherwise

10: t = t + 1

11: until Convergence

12: Select a batch of $b$ unlabeled videos based on the final solution $z_{t}$

####  III-A3 Computational Considerations

Computing the diversity matrix $R$ involves quadratic complexity. We first
note that $R$ needs to be computed only once in our framework, before the
start of the AL iterations. As the unlabeled videos get queried through AL, we
can keep deleting the corresponding rows and columns in $R$ to derive the new
diversity matrix. Moreover, random projection algorithms can be used to speed
up computations. The theory of random projections states that, if we have a
point cloud in a high dimensional space, they may be projected into a suitable
lower-dimensional space such that the distances between the points are
approximately preserved [48]. A data matrix $A\in\Re^{N\times D}$ in the $D$
dimensional space is multiplied by a random projection matrix
$X\in\Re^{D\times d}$ $(d\ll D)$ to obtain a projected matrix
$B\in\Re^{N\times d}$ in the lower dimensional space $d$: $B=AX$ [49]. This
can be used to substantially reduce the computational overhead, as distance
computations are more efficient in the low dimensional space. We will explore
this as part of future research.

###  III-B Active Frame Sampling

Once we select $b$ videos from the unlabeled set, our next task is to identify
a subset of $k$ frames from each of these videos; we exploited representative
sampling techniques for this purpose. These techniques identify the exemplar
data points which well-represent a given dataset. In particular, the coreset
algorithm selects a subset of points such that a model trained over the
selected subset is maximally similar to that trained on the whole dataset. For
the sake of completeness, we discuss the main ideas here and request
interested readers to refer to [16] for further details. Coreset poses the
subset selection problem as:

$\min_{s:|s|=k}\Bigg{|}\frac{1}{n}\sum_{i\in[n]}l(x_{i},y_{i},A_{i})-\frac{1}{|s|}\sum_{j\in
s}l(x_{j},y_{j},A_{j})\Bigg{|}$ (6)

where $(x_{i},y_{i})$ denotes a training sample and its label, $A_{i}$ denotes
a learning algorithm which outputs a set of parameters by minimizing a loss
function $l(.,.,.)$ on a given labeled set $i$. Informally, given a budget
$k$, the goal is to select a set of samples $s$, such that the model trained
on $s$ depicts similar performance as the model trained on the whole dataset
with $n$ samples.

This function cannot be directly optimized, as the labels of the samples in
the unlabeled set are unknown. An upper bound of this function was derived and
the problem of active sampling was shown to be equivalent to the $k$-center
problem (also called min-max facility location problem) [50]. The objective of
this problem is to select $k$ center points from $n$ samples, such that the
largest distance between a data point and its nearest center is minimized.
Formally, this can be posed as follows:

$\min_{s:|s|=k}\max_{i}\min_{j\in s}\Delta(x_{i},x_{j})$ (7)

This problem is NP-Hard [51]. However, a greedy algorithm, as detailed in
Algorithm 2, is guaranteed to produce a solution $s$ such that:
$\max_{i}\min_{j\in s}\Delta(x_{i},x_{j})\leq 2\times OPT$, where $OPT$ is the
optimal solution. We used this algorithm to select a subset of $k$ frames from
each of the queried videos. As evident from the formulation, our method does
not make any assumptions about the contents of the video, and is applicable to
any type of video.

Algorithm 2 The Active Frame Sampling Algorithm [16]

0: A video with $n$ frames ($x_{1},x_{2},\ldots x_{n}$) and frame budget $k$

1: Initialize $s=\Phi$

2: repeat

3: $u=\operatorname*{arg\,max}_{i\in[n]\backslash s}\min_{j\in
s}\Delta(x_{i},x_{j})$

4: $s=s\cup\\{u\\}$

5: until $|s|=k$

6: Select $k$ frames from the video based on the set $s$

##  IV Experiments and Results

###  IV-A Datasets

We used the UCF-101 [52] and the Kinetics datasets [53] to study the
performance of our algorithm. Both these datasets contain videos of humans
performing a variety of actions, captured under unconstrained, real-world
conditions, and are extensively used to study the performance of video
classification algorithms. We used data from $5$ classes at random from each
dataset for our experiments.

###  IV-B Oracle Simulation

All the publicly available video datasets contain annotations for the complete
videos; we did not find any datasets which contain annotations based on a
subset of frames. Also, different active sampling algorithms will select
different subsets of frames, and it is challenging to obtain annotations from
a human labeler for every possible subset of frames for a given video, to
conduct experiments. We therefore used a deep neural network to simulate the
human labeling oracle in our empirical studies. The oracle model was trained
on a completely different subset of the data. No information about the oracle
model was used in the design and development of our active learning algorithm.
During AL, when a video sample was selected for query, the selected frames
were passed as an input to the trained oracle model and its prediction entropy
on the sample was computed. If the entropy exceeded a particular threshold
$\tau_{oracle}$, the oracle was assumed to be not confident enough to produce
a label, and no label was returned; otherwise, the oracle returned the
predicted label (which may be correct or incorrect). These were done to
appropriately mimic a real-world data annotation setup with a human annotator.

###  IV-C Implementation Details

Base Model: We used a CNN-RNN architecture in our experiments where
InceptionV3 pretrained on the ImageNet-1k dataset was used as the feature
extractor and a GRU network as the decoder
222https://keras.io/examples/vision/video_classification/. The input frames
were scaled and normalized to a fixed input size of $224\times 224$ pixels and
fed into the Convolutional Neural Network (CNN). The features extracted were
fed into a $5$-layer GRU network which consists of $2$ GRU layers and $1$
fully connected layer with one dropout layer. The $2$ GRU layers had $20$ and
$12$ neurons, while the first fully connected layer had $8$ neurons with the
ReLU activation function. We used the adam optimizer with a learning rate of
$0.001$, momentum of $0.99$, batch size of $32$, and the network was trained
for $20$ epochs in each active learning iteration.

Oracle Model: We used a similar CNN-RNN architecture as the oracle model.
However, for the oracle model, the $2$ GRU layers of the GRU network had $40$
and $16$ neurons. We used the adam optimizer with a learning rate of $0.001$
for the UCF dataset and $0.01$ for the Kinetics dataset, momentum of $0.99$,
batch size of $64$, and the network was trained for $30$ epochs. As part of
future research, we plan to study the performance of our framework with other
architectures for the oracle model, and also conduct experiments with real
people as annotators.

###  IV-D Experimental Setup

Each dataset was split into $5$ parts: $(i)$ an initial training set $L$;
$(ii)$ unlabeled set $U$; $(iii)$ test set $T$; $(iv)$ training set to train
the oracle model $L_{oracle}$; and $(v)$ test set $T_{oracle}$ to test the
oracle model and compute the entropy threshold $\tau_{oracle}$. The number of
samples (videos) in each of these sets, together with the accuracy of the
oracle model ($A_{oracle}$) for each dataset are depicted in Table I. We note
that a better trained oracle could have potentially improved the performance
of our algorithm; however, we wanted to validate our algorithm in a
challenging real-world setup, where the annotators can abstain from labeling
samples and can also provide incorrect labels. We therefore used an oracle
model with moderate accuracy ($\approx 70-75\%$) in our empirical studies.

Dataset | L | U | T | $L_{oracle}$ | $T_{oracle}$ | $A_{oracle}$
---|---|---|---|---|---|---
UCF | 250 | 320 | 150 | 697 | 185 | $75.61\%$
Kinetics | 500 | 750 | 300 | 584 | 211 | $71.34\%$
TABLE I: Dataset split used in our experiments, together with the accuracy of
the oracle model for the two datasets.

(a) UCF

(b) Kinetics

Figure 2: AL performance comparison. The $x$-axis denotes the iteration number
and the $y$-axis denotes the accuracy on the test set. Best viewed in color.
UCF Dataset: Video budget $b=25$, Frame Budget $k=100$
---
Methods | Total Videos Queried | Correct (%) | Incorrect (%) | Discarded (%)
RR | 250 | $54.4\pm 1.38$ | $42.4\pm 1.6$ | $3.2\pm 0.8$
ER | 250 | $55.2\pm 1.05$ | $40.4\pm 1.05$ | $4.4\pm 0.69$
EK | 250 | $53.06\pm 1.40$ | $30.93\pm 2.20$ | $16\pm 0.8$
Proposed | 250 | $58.66\pm 1.8$ | $31.06\pm 0.61$ | $10.26\pm 2.41$
TABLE II: Performance of the oracle on the UCF dataset. Kinetics Dataset:
Video budget $b=25$, Frame Budget $k=100$
---
Methods | Total Videos Queried | Correct (%) | Incorrect (%) | Discarded (%)
RR | 250 | $67.33\pm 1.40$ | $23.33\pm 1.40$ | $9.33\pm 1.40$
ER | 250 | $64\pm 1.2$ | $23.86\pm 2.83$ | $12.13\pm 4.02$
EK | 250 | $64.66\pm 1.28$ | $25.06\pm 3.02$ | $10.26\pm 4.31$
Proposed | 250 | $66\pm 1.44$ | $22.93\pm 1.51$ | $11.06\pm 1.28$
TABLE III: Performance of the oracle on the Kinetics dataset.

The oracle model was trained on $L_{oracle}$; each sample in $T_{oracle}$ was
then passed as an input to the trained oracle and the prediction entropy was
noted. The $50^{th}$ percentile of the prediction entropy distribution was
taken as the entropy threshold $\tau_{oracle}$; during the AL iterations, if
the entropy of any queried video exceeded this threshold, the oracle was
assumed to abstain from labeling. The base model was first trained on the set
$L$. In each AL iteration, each algorithm queried $b$ videos from the set $U$,
and $k$ frames from each of the $b$ videos. The $k$ frames of each video were
then passed as an input to the oracle model. Based on its prediction entropy
on the sample, the oracle may or may not furnish a label for a given unlabeled
video sample. If the oracle does not furnish a label for a given video, it was
discarded. The other unlabeled videos (which were labeled by the oracle),
together with the returned labels were then appended to the training set, the
base model was updated, and its accuracy was computed on the test set. The
process was repeated for $10$ iterations, which was taken as the stopping
criterion in this work. All the results were averaged over $3$ runs (with
different training, unlabeled and test sets) to rule out the effects of
randomness. The video budget $b$ was taken as $25$ and the frame budget $k$ as
$100$ in each AL iteration. The weight parameter $\mu$ in Equation (3) was
taken as $0.01$ and a Gaussian kernel was used to compute the diversity matrix
in Equation (2).

###  IV-E Comparison Baselines

As mentioned in Section II, existing AL techniques for video classification
query the complete videos for annotation and the labels obtained are assumed
to be always correct. In our framework, the labeling oracle may refuse to
provide a label to a queried video and may also provide an incorrect label.
This is a more challenging and real-world scenario. It will thus not be fair
to compare our method against the existing techniques. We used three
comparison baselines in this work: $(i)$ Random-Random (RR), where we selected
a batch of $b$ videos at random and a subset of $k$ frames from each video at
random; $(ii)$ Entropy-Random (ER), where the $b$ videos with the highest
classification entropies were queried and $k$ frames were queried from each at
random; and $(iii)$ Entropy-kmeans (EK), where $b$ videos were first selected
using entropy sampling; $k$-means clustering was then performed and the $k$
frames corresponding to the $k$ cluster centroids were selected for query from
each video.

(a) Frame Budget k = 10

(b) Frame Budget k = 20

(c) Frame Budget k = 50

(d) Frame Budget k = 100

Figure 3: Study of the number of queried frames per video. The result with
$k=100$ (default setting) is the same as in Figure 2(a) and is included here
for the sake of completeness. Best viewed in color.

(a) Video Budget b = 15

(b) Video Budget b = 20

(c) Video Budget b = 25

(d) Video Budget b = 30

Figure 4: Study of the number of queried videos in each AL iteration. The
result with $b=25$ (default setting) is the same as in Figure 2(a) and is
included here for the sake of completeness. Best viewed in color.

###  IV-F Active Learning Performance

The AL performance results are depicted in Figure 2. In each figure, the
$x$-axis represents the iteration number, and the $y$-axis denotes the
accuracy on the test set. The proposed method comprehensively outperforms the
RR method on both datasets. The ER method depicts random fluctuations in the
test accuracy over the AL iterations; our method, on the other hand, depicts a
more steady growth in the test accuracy. The EK method depicts the best
performance among the baselines, but is not as good as our method. Our method
outperforms EK in most of the AL iterations across both the datasets. It also
attains the highest accuracy after $10$ AL iterations for both the datasets.
We can conclude the following: $(i)$ our video selection criterion based on
uncertainty and diversity identifies the most informative videos in the
unlabeled set; and $(ii)$ our frame selection criterion based on
representative sampling selects a subset of exemplar frames from each queried
video, so that a large percentage of them can be correctly annotated by the
oracle, which enriches the quality of our training data. As a result, our
method augments maximal useful information to the deep neural network, which
boosts its generalization capability. These results unanimously corroborate
the potential of our framework in substantially reducing the human annotation
effort in real-world video classification applications, where labeling a
single sample involves significant time and human labor.

The performance of the oracle model is reported in Tables II and III for the
UCF and Kinetics datasets respectively. A total of $250$ videos were queried
from these datasets ($25$ videos in each of the $10$ AL iterations). The
tables show the percentage of these videos that were correctly annotated,
incorrectly annotated and discarded by the labeling oracle. For the UCF
dataset, and for the proposed method, the oracle correctly annotated $58.66\%$
of the queried videos (the highest among all the methods). This shows that
representative sampling through coreset is an effective strategy to identify
the exemplar frames from a queried video, which have a high probability of
receiving the correct label from the oracle, and augmenting useful information
to the training set. For the Kinetics dataset, $66\%$ of the videos queried by
our method were correctly annotated by the oracle, where as $67.33\%$ of the
videos queried by Random Sampling were annotated correctly by the oracle.
However, we note that, having a high percentage of unlabeled videos correctly
labeled by the oracle does not necessarily mean that the useful samples are
being queried. For instance, it is easy to select a batch of videos, which do
not have much useful content and are easy to label, and get a high percentage
of them correctly labeled by the oracle. However, these videos, even if
correctly labeled, will not augment much useful information to the training
set, as they are devoid of any useful content. Even though RR depicts a
slightly higher percentage of correctly labeled samples than our method in
Table III, its generalization accuracy is much worse than our method, as
evident from Figure 2(b). The key challenge is to query a set of informative
videos and get a high percentage of them correctly labeled by the oracle; both
of these are crucial in improving the generalization capability of the model
over time. The results in Figure 2 jointly capture both these aspects, and
show that our method outperforms the baselines.

###  IV-G Effect of the Number of Queried Frames per Video

In this experiment, we studied the effect of the frame budget $k$ (number of
frames allowed to be selected from a queried video) on the AL performance. The
results on the UCF dataset, with frame budgets $10$, $20$, $50$ and $100$ are
presented in Figure 3. Our method depicts impressive performance across
different frame budgets. For frame budgets $20$, $50$ and $100$, our framework
attains the highest test accuracy after $10$ AL iterations. Note that querying
lesser number of frames from a video lessens the labeling burden on the
oracle, as the oracle has to review an even smaller number of frames to
furnish a label. These results show the promise and potential of our technique
to further reduce human annotation effort in a video classification
application.

###  IV-H Effect of the Number of Queried Videos

The goal of this experiment was to study the effect of the video budget $b$
(number of videos queried in each AL iteration) on the AL performance. The
results on the UCF dataset with $b=15,20,25$ and $30$ are shown in Figure 4.
Our framework once again surpasses the baselines across the different budgets.
These results are important from the standpoint of a real-world application,
where the batch size is governed by the time, man-power and other available
resources in a given application, and is different for different applications.

##  V Conclusion and Future Work

The goal of this research was to devise an efficient annotation mechanism to
reduce human annotation effort in video classification applications, where
annotating a single data instance is extremely tedious. Our framework
identifies a batch of informative videos, together with a set of exemplar
frames from each; the human annotator has to produce a label for each video
just by reviewing the subset of frames, instead of watching the complete video
end-to-end. To the best of our knowledge, this is the first research effort to
develop an AL technique for video classification, with this kind of a query
and annotation mechanism. Our empirical results validated the promise and
potential of our framework to drastically reduce human annotation effort in
training a deep neural network for video classification. We hope this research
will motivate the development of AL algorithms with other annotation
mechanisms, with the goal of further reducing the human annotation effort in
video classification.

As part of future work, we plan to validate the performance of our algorithm
on other applications where the data has a temporal nature. For instance, the
proposed query mechanism will also be very relevant in a text classification
application to identify informative text snippets, so that a human annotator
can furnish a label by reviewing only the snippets, rather than reading the
document end-to-end. We will also study the performance of our framework with
different size of the data splits, as outlined in Table I.

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# Pressure Tuning of Berry Curvature in CrGeTe3

G. Scharf  Raymond and Beverly Sackler School of Physics and Astronomy, Tel-
Aviv University, Tel Aviv, 69978, Israel  B. Hen  Raymond and Beverly Sackler
School of Physics and Astronomy, Tel-Aviv University, Tel Aviv, 69978, Israel
P. M. Sarte  Materials Department, University of California, Santa Barbara,
California, 93106, USA  B. R. Ortiz  Materials Department, University of
California, Santa Barbara, California, 93106, USA  G. Kh. Rozenberg  Raymond
and Beverly Sackler School of Physics and Astronomy, Tel-Aviv University, Tel
Aviv, 69978, Israel  S. D. Wilson  Materials Department, University of
California, Santa Barbara, California, 93106, USA  A. Ron  Raymond and Beverly
Sackler School of Physics and Astronomy, Tel-Aviv University, Tel Aviv, 69978,
Israel

###### Abstract

The integrated Berry curvature is a geometric property that has dramatic
implications on material properties. This study investigates the integrated
Berry curvature and other scattering mechanisms through their contribution to
the anomalous Hall effect in CrGeTe3. The Anomalous Hall effect is absent in
the insulating phase of CrGeTe3 and evolves with pressure in a dome-like
fashion as pressure is applied. The dome’s edges are characterized by Fermi
surface deformations, manifested as mixed electron and hole transport. We
discuss the possibility that in CrGeTe3 the integrated Berry curvature is
tuned by the application of hydrostatic pressure due to its relation to the
Fermi surface deformations.

For electrons in solids, Berry phase is a geometric property of the band
structure that has dramatic implications on materials’ properties [1]. It is
acquired when a system is subject to a cyclic adiabatic transformation in its
parameter space, and it is determined by the integrated Berry curvature. As a
band-structure property, one may conjecture that dramatic changes to the Fermi
surface will result in considerable changes to the Berry curvature and thus
may result in variation of its integrated value. An extreme case of such a
change would be the metal-insulator transition [2], where at the insulating
state, there is no Fermi surface, and at the metallic state, a Fermi surface
forms, which may host a nonzero integrated Berry curvature. Such effects were
recently observed and understood theoretically in graphene moiré superlattices
where Berry curvature features were tuned by varying the electric displacement
field and carrier density [3, 4].

A common manifestation of the Berry phase in electronic transport properties
is the anomalous Hall effect (AHE). The AHE is an additional contribution to
the transverse resistivity ($\rho_{xy}$) on top of the ordinary Hall effect.
It occurs in materials where time-reversal symmetry is broken in the presence
of spin-orbit interaction [5]. As such, it can be probed through measurements
of $\rho_{xy}$ as a function of the magnetic field and serve as a superb probe
for investigating the integrated Berry curvature of electrons in solids. We
chose CrGeTe3, a ferromagnetic insulator undergoing a metal-insulator
transition with the application of hydrostatic pressure, as a laboratory for
investigating the evolution of the integrated Berry curvature when the Fermi
surface is strongly deformed.

CrGeTe3 is a layered ferromagnetic insulator with a Curie temperature (TCurie)
of $\mathrm{\sim 67\ K}$ [6] which has recently attracted a lot of attention.
Inelastic neutron scattering suggests that CrGeTe3 is a topological magnonic
insulator [7] that is predicted to sustain ferromagnetism to the 2D limit [8,
9, 10]. Additionally, short-range fluctuations seem to play an important role
above the transition temperature [11, 12], in great similarity to the closely
related compound CrSiTe3 [13, 14, 15].

Application of hydrostatic pressure changes TCurie of CrGeTe3. Up to
$\mathrm{4.5\ GPa}$, the Curie temperature decreases as pressure is applied
[16, 2]. Above $\mathrm{4.5\ GPa}$, TCurie increases dramatically, rising from
$\mathrm{\sim 54\ K}$ to $\mathrm{\sim 250\ K}$ at $\mathrm{9.1\ GPa}$ [2].
Within this pressure range, CrGeTe3 undergoes a metal-insulator transition at
$\mathrm{\sim 6\ GPa}$ [2]. The coexistence of time-reversal symmetry breaking
with the metal-insulator transition in a material with a high Z element (Te)
makes CrGeTe3 an ideal candidate to search for an evolution of the Berry
curvature as the system is tuned through the metal-insulator transition.

In this letter, we demonstrate that the various contributions to the AHE in
CrGeTe3 can be tuned by hydrostatic pressure which at certain pressures is
present also above $\mathrm{300\ K}$ suggesting possible enhancement of
TCurie. Measurements of the AHE at a wide range of hydrostatic pressures at
T=2K reveal a dome-like behavior that onsets at the metal-insulator transition
and is quenched towards higher pressures. The AHE dome coincides with the
pressure range where Fermi surface deformations are observed through the
ordinary Hall effect. We discuss a scenario where the AHE dome emerges due to
the appearance of a nonzero integrated Berry curvature due to the observed
Fermi surface deformation.

##  I Methods

To create the CrGeTe3 crystals, Cr powder (99.95%, alfa), Ge powder
(99.9999%), and Te lump (99.999+%) were sealed in a fused silica ampule at an
approximate ratio of 1:1:8 Cr:Ge:Te. Fluxes were heated to 900°C at a rate of
200°C/hr, soaked at 900°C for 24h, and then slowly cooled down to 550°C at a
rate of 2°C/h. The resulting fluxes were centrifuged at 550°C to remove molten
Te from the crystals, after which thin platelets of dimensions 1mm x 2mm x
0.1mm were mechanically isolated.

The pressure was exerted on the samples using miniature diamond anvil cells
(DACs) [17], with diamond anvil culets of $\mathrm{300\ \mu m}$. A rhenium
gasket was drilled, then filled and covered with a powder layer of 75% Al2O3
and 25% NaCl for electrical insulation. Two pressure cells were loaded with
$\mathrm{\sim 5\ \mu m}$ thick CrGeTe3 flakes and placed on top of the
insulating layer, which functions as a pressure-transmitting medium. A
$\mathrm{\sim 5\ \mu m}$ thick Pt foil was cut into triangular pieces and
placed in contact with the CrGeTe3 flakes allowing electrical transport
measurements at elevated pressures in the Van der Pauw geometry. As such, the
$\rho_{xx}$ and $\rho_{xy}$ are inferred from the measured resistance up to a
factor of order unity due to uncertainties in the sample geometry and
thickness, which are inevitable inside a DAC. In addition, Ruby fragments were
placed between the Pt leads for pressure determination [18]. The samples were
compressed in steps of $\mathrm{2-4\ GPa}$ and then cooled down from ambient
temperature to $\mathrm{2\ K}$. Measurements from the two cells are shown in
this manuscript.

##  II Results and Discussion

Figure 1(a) shows that gradual application of pressure results in a
significant drop to the sample resistance, which begins to saturate at
pressures of $\mathrm{\sim 6\ GPa}$ where a metal-insulator transition occurs
in agreement with Ref. [19, 2] (see sections S1 and S4 in the supplementary
material for resistivity versus temperature plots). TCurie as a function of
pressure from our AHE at high temperatures, is also shown in Figure 1(a) and
will be discussed later in this section. We note that the pressure in which we
observe the metal-insulator transition, and the dependence of Curie
temperatures on pressure, are consistent with previous reports, indicating the
similarity in sample quality and pressure conditions.

The inset of Figure 1(a) shows the Hall coefficient as a function of pressure
at $\mathrm{T=2\ K}$, which exhibits a dome-like behavior as a function of
pressure. In the metallic state (at $\mathrm{6<P<14.5\ GPa}$), the negative
sign of the Hall coefficient indicates that transport is electron-dominated at
all temperatures, as it is demonstrated for $\mathrm{10.6\ GPa}$ in Figure
1(b). The full data set is available in section S5. Figure 1(c) shows that at
the edges of the dome, both electrons and holes contribute to transport, as
can be seen by the flattening and sign change of the Hall slope as a function
of temperature. We note that a similar behavior also occurs at other pressures
in the vicinity of $\mathrm{14\ GPa}$, as shown in supplementary sections S5
and S6. These most likely originate from hole-like Te 5p and electron-like Cr
3d bands. It should be mentioned that measurements of the Hall effect were
infeasible at pressures below $\mathrm{3.2\ GPa}$ due to the large
longitudinal resistivity relative to the magnitude of the Hall effect
(supplementary material section S6).

Figure 1: The left axis of the panel (a) shows a significant decrease in the
samples’ resistance as pressure is applied due to the metal-insulator
transition. The Blue and red points represent measurements from cells 1 and 2,
respectively. The values are scaled by a single geometric factor of order
unity, which is used throughout the manuscript for any longitudinal
resistivity measurement. On the right axis, we show the Curie temperature as a
function of applied pressure. The cyan dots represent measurements from this
work based on the AHE, which extend the pressure range covered in Ref. [2],
shown in green. The uncertainties in the value of TCurie are estimated as the
interval between our sampling points, and the pressure uncertainties are
estimated to be about $\mathrm{0.5\ GPa}$. The arrows signify that the value
of $\mathrm{300\ K}$ is a lower bound for the Curie point, as it is the
highest temperature in which data was taken. The inset shows the Hall
coefficient as a function of applied pressure at $\mathrm{2\ K}$ extracted
from a linear fit in the field range between $\mathrm{4\ kOe}$ and
$\mathrm{10\ kOe}$. The Blue and the red points are measurements of
$\rho_{xy}$ from the first and second cells, respectively. A single geometric
factor of order unity was used to scale the values of $\rho_{xy}$ here and
throughout the manuscript. Panels (b) and (c) show the antisymmetrized Hall
measurements at pressures of $\mathrm{10.6\ GPa}$ and $\mathrm{3.2\ GPa}$ at
different temperatures as a function of the magnetic field at the range which
was used to calculate the Hall coefficient.

Above $\mathrm{5.6\ GPa}$, when CrGeTe3 enters the metallic state, a
significant AHE signal is observed. Figure 2(a) shows a characteristic
behavior of the AHE in the intermediate pressure regime. Here, the AHE is the
strongest at low temperatures and monotonically weakens as the temperature
increases. From this data, it is clear that the AHE persists to much higher
temperatures than the Curie temperature (TCurie) under ambient pressure
($\mathrm{\sim 67\ K}$), which we interpret as an increase of TCurie. We note
that the AHE can also occur in paramagnetic materials [20, 21], and therefore
its presence at high temperatures does not necessarily signify enhancement of
TCurie. However, we find this scenario less likely since the trend observed by
our measurements is a smooth continuation of the trend observed by
magnetometry measurements [2] shown in Figure 1(a). Figure 2(b) shows
measurements of the AHE at $\mathrm{13.5\ GPa}$, characteristic of the high-
pressure regime between $\mathrm{13.5}$ and $\mathrm{17.6\ GPa}$. At low
temperatures (blue curves), the AHE is completely absent from measurements of
$\rho_{xy}$, as can be seen by the absence of the steep low field AHE slope.
As the temperature increases, the AHE gradually appears and is enhanced at
elevated temperatures. We note that the AHE does not decay even at room
temperature, continuing the trend observed in Ref. [2], thus possibly
indicating that TCurie in CrGeTe3 surpasses room temperature at this pressure
range.

Figure 2: Measurements of the Hall effect in sample 1 at different
temperatures at pressures of $\mathrm{10.6\ GPa}$ (panel (a)) and
$\mathrm{13.5\ GPa}$ (panel (b)). The steep slopes at low fields are due to
the AHE.

The qualitative differences in the evolution of the AHE with temperature
suggest that different mechanisms are at play in different pressure regimes.
To disentangle the contributions to the AHE, we separate the anomalous Hall
resistivity $\rho_{AHE}$ to intrinsic and extrinsic sources and follow Hou
$et\ al.$ [22] who further distinguish between extrinsic scattering
originating from static (temperature independent) and dynamic (temperature
dependent) scattering mechanisms. At low temperatures, quasiparticles are
frozen, and there are no dynamic scattering events. Therefore,
$\rho_{AHE}$$(T=0)$ is a static effect, either intrinsic with geometric
origins or extrinsic emanating from disorder. At higher temperatures,
quasiparticles are thermally activated and dynamic extrinsic scattering
mechanisms may contribute to the AHE. We turn to interpret our results in
light of these distinctions in the three pressure regimes.

$\rho_{AHE}$ is extracted using a procedure similar to Ref. [23] detailed in
the supplementary section S3. In the insulating state, the values of the
longitudinal resistance ($\mathrm{R_{xx}}$) intermixed in the measurements of
$\mathrm{R_{xy}}$ are dominant for fields smaller than $\mathrm{0.2\ T}$ (see
section S6 in the supplementary material). Therefore, our measurements are
insensitive to small anomalous Hall signals at this pressure range. Figure 3
shows $\rho_{AHE}$ as a function of temperature for various pressures above
$\mathrm{5.6\ GPa}$. At the intermediate pressure regime, at low temperatures,
$\rho_{AHE}$ $\neq 0$. This suggests that the pressure tuning into the
metallic state activates either an intrinsic contribution to the AHE or a
static extrinsic scattering mechanism. The signal decays as the temperature
increases, and its disappearance marks the Curie temperature. In the high-
pressure regime ($\mathrm{P>13.5\ GPa}$), $\rho_{AHE}$ $=0$ at low
temperatures and smoothly increases as the temperature increases, persisting
up to room temperature above which we could not heat our DACs. The absence of
AHE indicates either a perfect cancellation of contributions from various
scattering mechanisms, meaning that the sum of all the various contributions
to the AHE is zero, or the complete nullification of all of them. Perfect
cancellation typically occurs when two mechanisms contribute to $\rho_{AHE}$
with opposite signs, which typically occurs at a specific temperature as seen,
for example, in Ref. [24, 25, 26, 27]. In contrast, in CrGeTe3 at
$\mathrm{P>13\ GPa}$, $\rho_{AHE}$ $=0$ at a wide temperature range (over
$\mathrm{150\ K}$ at $\mathrm{17.6\ GPa}$) rather than crossing zero at a
particular temperature. Perfect accidental cancellation of various scattering
mechanisms at such a wide temperature range is unlikely. Therefore we deduce
that for $\mathrm{P>13\ GPa}$, all scattering mechanisms are negligible at low
temperatures, and the behavior shown in Figure 3 is dominated by scattering
off of thermally activated quasi-particles.

Figure 3: $\rho_{AHE}$ as a function of temperature for various pressures,
measured in sample 2. A similar plot for sample 1 is shown in S2 in the
supplementary material. At the intermediate pressure regime, between the
metal-insulator transition and $\mathrm{13\ GPa}$, $\rho_{AHE}$ $\neq 0$ at
low temperatures and decays smoothly as the temperature increases. In
contrast, in the high-pressure regime, at low temperatures $\rho_{AHE}$ $=0$,
and increases as the temperature increases.

In Figure 4, we plot $\rho_{AHE}$ at $\mathrm{2\ K}$ as a function of the
pressure, which exhibits a dome-like shape starting at the metal-insulator
transition and finishing around $\mathrm{13\ GPa}$. To the best of our
knowledge, this behavior has not been observed in the past. Typically,
ferromagnets exhibit a monotonic behavior of the AHE as a function of
pressure, as seen, for example, in $\mathrm{CeAlSi}$ [28] and
$\mathrm{Co_{3}Sn_{2}S_{2}}$ [29], where in the former, the AHE is generated
by skew scattering and in the latter by the intrinsic Berry phase. To
understand this behavior, we look at the relation between $\rho_{AHE}$ and
$\rho_{xx}$, which at low temperatures, in the absence of dynamic scattering,
simplifies to [22]:

$\rho_{AHE}=\alpha\rho_{xx}+\beta_{0}\rho_{xx}^{2}$ (1)

where $\alpha$ represents contributions from skew scattering, and $\beta_{0}$
is a mixture of intrinsic and static side jump mechanisms. In our experiment,
we tune $\rho_{xx}$ by changing the hydrostatic pressure P. The inset to
Figure 4 shows $\rho_{AHE}$ as a function of $\rho_{xx}$$(P)$ at a constant
temperature $\mathrm{T=2\ K}$, where a clear non-parabolic hysteretic behavior
is observed. This means that the application of pressure changes not only
$\rho_{xx}$ but also $\alpha$ and $\beta_{0}$. Transport measurements alone
could not disentangle the contributions of the intrinsic and the static
extrinsic mechanisms to the AHE. However, in CrGeTe3, the AHE dome onsets and
ends in regimes where mixed transport of electrons and holes is observed
(Figure 1(a) inset), indicative of Fermi surface deformations. We suggest that
the Fermi surface deformations result in the appearance of nonzero integrated
Berry curvature, which is in some similarity to what has been observed in
graphene moiré superlattices [3, 4]. Generally, the intrinsic contribution to
the AHE depends on the integrated Berry curvature over all occupied states
[1]. Therefore, it can be tuned by either changing the Fermi level or by
causing changes to the band structure that manifest changes to the Berry
curvature. A toy model calculation, based on Ref [30], where the integrated
Berry curvature is tuned by changing the Fermi energy can be found in Ref [1].
Similar phenomena were observed experimentally and understood theoretically in
graphene moiré superlattices, where both the band structure and the Fermi
level were changed[3, 4]. We suggest that in CrGeTe3, at the metal-insulator
transition, the effects of nonzero integrated Berry’s curvature appear,
contributing to $\rho_{AHE}$ at low temperatures. As pressure increases, those
effects get stronger due to the hybridization of the Te and the Cr bands,
which mark the rising part of the AHE dome. As the pressure further increases,
the AHE weakens and goes to zero at the point where mixed electron/hole
transport is observed again. We note that at the end of the AHE dome,
structural anomalies were observed by x-ray diffraction as nonmonotonic
behavior of the $\angle$Te-Cr-Te angle [19]. In light of the nonmonotonic
behavior of atomic positions, one may not be surprised by the nonmonotonic
behavior we report in the electronic properties of CrGeTe3. Additionally, Dong
and coauthors [31] observed a kink in the axial ratio at $\mathrm{14\ GPa}$ in
CrGeTe3 and claim it is indicative of an isostructural phase transition, which
is possibly related to the mixed electron-hole transport we observe. It is
worth noting that the behavior depicted in Figure 4 can also be explained
through changes in the skew-scattering and static side jumps contributions to
the AHE as a function of the pressure. However, since the AHE dome onsets at
the metal-insulator transition and ends where another Fermi surface
deformation was observed strengthens our belief that the observed dome-like
behavior of the AHE at low temperatures is due to changes in the integrated
Berry’s curvature tuned by the application of hydrostatic pressure.

Figure 4: $\rho_{AHE}$ measured at $\mathrm{2\ K}$ as a function of applied
pressure. The black line is a guide to the eye. The inset shows $\rho_{AHE}$,
measured at $\mathrm{2\ K}$, as a function of the longitudinal resistivity
$\rho_{xx}$, showing a hysteretic behavior that deviates from the parabolic
relation in equation 1. The Blue and the red points are from the first and
second cells, respectively. Their resistivity values are scaled by a single
geometric factor of order unity, as was previously mentioned in Figure 1.

In summary, we have measured the AHE in CrGeTe3 as a function of applied
hydrostatic pressure and temperature. We suggest that the application of
hydrostatic pressure to CrGeTe3 results in the tuning of the intrinsic
contribution to the AHE, which originates from nonzero integrated Berry’s
curvature in proximity to the metal-insulator transition and a Fermi surface
deformation. We also found that at elevated pressures, the AHE appears at
temperatures above $\mathrm{300\ K}$ suggestive of possible enhancement of
TCurie, continuing and agreeing with the trend observed by Bhoi $et\ al.$ [2].

###### Acknowledgements.

A. R. acknowledges support from the Zuckerman Foundation, and the Israel
Science Foundation (Grant No. 1017/20). S.D.W., B.R.O., and P.S. gratefully
acknowledge support via the UC Santa Barbara NSF Quantum Foundry funded via
the Q-AMASE-i program under award DMR-1906325. G. Kh. R. acknowledges the
Israel Science Foundation (Grants No. 1748/20). G. S. thanks Shay Sandik, Itai
Silber, and Gal Tuvia for the help with the cryogenic equipment.

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##  III Supplemental material

##  Appendix S2 -  S1 - The MIT - R(T) at different pressures

The longitudinal resistance as a function of temperature, normalized at
$\mathrm{9.5\ K}$, measured at different pressures in the first cell. The
change in the behavior of the graph from decreasing to increasing as a
function of the temperature in the low-temperature regime by application of
pressure indicates a metal-insulator transition driven by the application of
pressure on the CrGeTe3.

Figure S1: The longitudinal resistance as a function of temperature,
normalized at $\mathrm{9.5\ K}$, measured at different pressures in the first
cell. The change in the behavior of the graph from decreasing to increasing as
a function of the temperature by application of pressure indicates a MIT
driven by the application of pressure on the CrGeTe3.

##  Appendix S3 -  S2 - THE AHE measured in the first cell

Here we present our measurements of $\rho_{AHE}$ as a function of temperature
for the various pressures measured in the first sample. As was also observed
in the second sample ( in the main text), At pressures below $\mathrm{13\
GPa}$, $\rho_{AHE}\neq 0$ at low temperatures and decays smoothly as the
temperature increases. In contrast, for $\mathrm{P>13\ GPa}$, at low
temperatures $\rho_{AHE}=0$, and increases as the temperature increases.

Figure S2: $\rho_{AHE}$ as a function of temperature for the various pressures
measured for sample 1.

##  Appendix S4 -  S3 - The extraction of $\rho_{AHE}$ from the measurements

$\rho_{AHE}$ in a specific temperature and pressure, is extracted from the
measurements by measuring $\mathrm{R_{xy}(H)}$ and antisymmetrize it. This
results in graphs as shown in section S6. Then we do a linear fit to the high-
field regime ($\mathrm{4\ kOe<H}$), and the intersection of the fit with the
y-axis is the AHE resistance ($\mathrm{R_{AHE}}$) (see Fig.S3). Finally, by
multiplying $\mathrm{R_{AHE}}$ with the width of the sample, we get
$\rho_{AHE}$.

Figure S3: Here we show an example of how we extracted the AHE resistance
($\mathrm{R_{AHE}}$) for each pressure at different temperatures. The figure
displays the antisymmetrization of the raw data of $\mathrm{R_{xy}}$ as a
function of the applied field H, measured at several different temperatures
for the first sample in the metallic state at a pressure of $\mathrm{13.5\
GPa}$. The solid lines are the linear fit for the high-fields regime
($\mathrm{4\ kOe<H}$) at each temperature, and the big dots represent the
intersection of each fit with the y-axis. The intersection of each fit is
$\mathrm{R_{AHE}}$ measured at each temperature.

##  Appendix S5 -  S4 - $\rho_{xx}$ at different pressures and temperatures

In Figure S5, we present the longitudinal resistivity as a function of
temperature in the metallic state, measured on both samples. As can be seen,
they show very similar behavior as all of them are monotonic - increasing with
the temperature and showing similar values. As such, going back to our
measurements of the AHE as a function of the temperature (see Figures 2 and
S2), the observed behaviors cannot be explained just by the scaling of
$\rho_{AHE}$ with the $\rho_{xx}$. First, the scaling of $\rho_{xx}$ cannot
explain the change in the behavior of the AHE between the intermediate
pressure regime ($\mathrm{5.6\ GPa<P<13\ GPa}$) and the high-pressure regime
($\mathrm{13\ Gpa<P}$). Second, it cannot explain why at $\mathrm{13.5\ GPa}$,
the AHE is stronger than at higher pressures in the first sample and thus
probably not also in the second cell. Finally, going back to the low-
temperature behavior of the AHE as shown in Figure 4, the values of
$\rho_{xx}$ at low temperatures (shown in log-scale in Figure S6) cannot
explain the dome-like behavior of the AHE.

Figure S4: The longitudinal resistivity as a function of temperature at
different pressures presented in log-scale, on the left in the first sample
and on the right in the second sample. Figure S5: The longitudinal resistivity
as a function of temperature at different pressures in the metallic state, on
the left in the first sample and on the right in the second sample.

Figure S6: The longitudinal resistivity at low temperature (2K) as a function
of the pressure in log-scale. The Blue and the red points are from the first
and second cells, respectively. Their resistivity values are scaled by a
single geometric factor of order unity which was used throughout the
manuscript for each longitudinal measurement.

##  Appendix S6 -  S5 - The Hall slopes measured at different pressures and
temperatures

Here, we present the Hall slopes as a function of temperature, measured at
different pressures. In most measurements, the Hall slope is negative, meaning
that although there is a mix of electrons and holes in all pressures, in most
of the pressures, we can treat the transport as of electron-like charge
carriers. However, at $\mathrm{3.2\ GPa}$ and at $\mathrm{14.5\ GPa}$, there
is a change in the sign of the Hall slope, indicating that these pressures,
both holes and electrons contribute to the transport where their contributions
are temperature dependent, which can be seen in Figure 1(b) in the text as
well. At these pressures, we cannot treat the transport as dominated by a
single charge carrier. The fact that the Hall slope changes sign as a function
of the temperature at those pressures but not before might indicate changes in
the band structure of the CrGeTe3, which may result in a change in the
integrated Berry curvature.

Figure S7: The Hall slopes as a function of temperature, measured at
$\mathrm{0.87\ GPa}$ and at $\mathrm{3.2\ GPa}$. Figure S8: The Hall slopes as
a function of temperature, measured at $\mathrm{0.87\ GPa}$, $\mathrm{3.2\
GPa}$, and $\mathrm{5.6\ GPa}$. Figure S9: The Hall slopes as a function of
temperature, measured at different pressures. The dots represent data from the
first cell, and the Xs denote measurements from the second cell.

##  Appendix S7 -  S6 - Raw Data measurements of $\mathrm{R_{xy}}$ and the
resulted anti-symmetric plots for all pressures and cells

Here we present measurements of $\mathrm{R_{xy}}$ as a function of the applied
field H at various pressures and temperatures, both in their raw form and
after undergoing antisymmetrization. When there is significant mixing of
$\mathrm{R_{xx}}$ and $\mathrm{R_{xy}}$ in the measurements, it is reflected
in the raw data, which appears neither symmetric nor antisymmetric. This
effect has been observed multiple times, particularly in the low-pressure
regime before the metal-insulator transition.

Figure S10: The left panel displays the raw data of $\mathrm{R_{xy}}$ as a
function of the applied field H for the first sample in the metallic state at
a pressure of $\mathrm{7.5\ GPa}$. The right panel shows the same data after
antisymmetrization. Figure S11: The left panel displays the raw data of
$\mathrm{R_{xy}}$ as a function of the applied field H for the first sample in
the metallic state at a pressure of $\mathrm{9.5\ GPa}$. The right panel shows
the same data after antisymmetrization. Figure S12: The left panel displays
the raw data of $\mathrm{R_{xy}}$ as a function of the applied field H for the
first sample in the metallic state at a pressure of $\mathrm{10.6\ GPa}$. The
right panel shows the same data after antisymmetrization. Figure S13: The left
panel displays the raw data of $\mathrm{R_{xy}}$ as a function of the applied
field H for the first sample in the metallic state at a pressure of
$\mathrm{11.7\ GPa}$. The right panel shows the same data after
antisymmetrization. Figure S14: The left panel displays the raw data of
$\mathrm{R_{xy}}$ as a function of the applied field H for the first sample in
the metallic state at a pressure of $\mathrm{13.5\ GPa}$. The right panel
shows the same data after antisymmetrization. Figure S15: The left panel
displays the raw data of $\mathrm{R_{xy}}$ as a function of the applied field
H for the first sample in the metallic state at a pressure of $\mathrm{14.5\
GPa}$. The right panel shows the same data after antisymmetrization. Figure
S16: The left panel displays raw data of $\mathrm{R_{xy}}$ as a function of
the applied field H for the second sample in the insulating state at a
pressure of $\mathrm{0.87\ GPa}$. The right panel presents the same data after
antisymmetrization. The presence of significant intermixing between
$\mathrm{R_{xx}}$ and $\mathrm{R_{xy}}$ can be easily identified by the
absence of symmetry or antisymmetry in the raw data. Figure S17: The left
panel displays raw data of $\mathrm{R_{xy}}$ as a function of the applied
field H for the second sample in the insulating state at a pressure of
$\mathrm{3.2\ GPa}$. The right panel presents the same data after
antisymmetrization. The presence of significant intermixing between
$\mathrm{R_{xx}}$ and $\mathrm{R_{xy}}$ can be easily identified by the
absence of symmetry or antisymmetry in the raw data. Figure S18: The left
panel displays the raw data of $\mathrm{R_{xy}}$ as a function of the applied
field H for the second sample in the metallic state at a pressure of
$\mathrm{5.6\ GPa}$. The right panel shows the same data after
antisymmetrization. Figure S19: The left panel displays the raw data of
$\mathrm{R_{xy}}$ as a function of the applied field H for the second sample
in the metallic state at a pressure of $\mathrm{7.5\ GPa}$. The right panel
shows the same data after antisymmetrization. Figure S20: The left panel
displays the raw data of $\mathrm{R_{xy}}$ as a function of the applied field
H for the second sample in the metallic state at a pressure of $\mathrm{8.9\
GPa}$. The right panel shows the same data after antisymmetrization. Figure
S21: The left panel displays the raw data of $\mathrm{R_{xy}}$ as a function
of the applied field H for the second sample in the metallic state at a
pressure of $\mathrm{10.8\ GPa}$. The right panel shows the same data after
antisymmetrization. Figure S22: The left panel displays the raw data of
$\mathrm{R_{xy}}$ as a function of the applied field H for the second sample
in the metallic state at a pressure of $\mathrm{13.5\ GPa}$. The right panel
shows the same data after antisymmetrization. Figure S23: The left panel
displays the raw data of $\mathrm{R_{xy}}$ as a function of the applied field
H for the second sample in the metallic state at a pressure of $\mathrm{17.6\
GPa}$. The right panel shows the same data after antisymmetrization.


# Newsalyze: Enabling News Consumers to Understand Media Bias

Felix Hamborg1, Anastasia Zhukova2, Karsten Donnay3,4, Bela Gipp2 1Dept. of
Computer and Information Science, University of Konstanz, Germany,
<EMAIL_ADDRESS>2Data & Knowledge Engineering Group, University
of Wuppertal, Wuppertal, Germany<EMAIL_ADDRESS>3Dept. of Political
Science, University of Zurich, Switzerland<EMAIL_ADDRESS>4Dept. of
Politics and Public Administration, University of Konstanz, Germany,
<EMAIL_ADDRESS>
(2020)

###### Abstract.

News is a central source of information for individuals to inform themselves
on current topics. Knowing a news article’s slant and authenticity is of
crucial importance in times of “fake news,” news bots, and centralization of
media ownership. We introduce _Newsalyze_ , a bias-aware news reader focusing
on a subtle, yet powerful form of media bias, named bias by word choice and
labeling (WCL). WCL bias can alter the assessment of entities reported in the
news, e.g., “freedom fighters” vs. “terrorists.” At the core of the analysis
is a neural model that uses a news-adapted BERT language model to determine
target-dependent sentiment, a high-level effect of WCL bias. While the
analysis currently focuses on only this form of bias, the visualizations
already reveal patterns of bias when contrasting articles (overview) and in-
text instances of bias (article view).

††journalyear: 2020††copyright: rightsretained††conference: Proceedings of the
ACM/IEEE Joint Conference on Digital Libraries in 2020; August 1–5, 2020;
Virtual Event, China††booktitle: Proceedings of the ACM/IEEE Joint Conference
on Digital Libraries in 2020 (JCDL ’20), August 1–5, 2020, Virtual Event,
China††doi: 10.1145/3383583.3398561††isbn: 978-1-4503-7585-6/20/06††copyright:
none

(0cm,1cm) This is a preprint. The accepted manuscript can be found at:
https://doi.org/10.1145/3383583.3398561

##  1\. Introduction and Related Work

People rely on the news to inform themselves on current topics and events.
Especially news articles, which the public commonly deems most trustworthy
(Urban, 1999), are a central part of individual and societal opinion formation
and decision making. Media bias, e.g., slanted or biased news coverage, thus
can have severe effects on democratic processes (Meyrowitz, 1986). A subtle,
yet powerful form of media bias is bias by word choice and labeling (WCL),
which occurs when news authors sway readers’ perception of persons, actions,
or other semantic concepts by using different terms or phrases to refer to the
concepts, e.g., ”undocumented immigrant” vs. ”illegal alien.” Previous works
have struggled to automatically identify WCL bias (Balahur et al., 2013;
Godbole et al., 2007; Hamborg et al., 2019a), mainly due its implicitness,
subjectivity, and high context dependence (Card et al., 2015), requiring
actual understanding of the text at hand. However, the advent of deep learning
and language models, such as BERT, has led to a significant leap towards
natural language understanding (NLU), thereby strongly improving the
performance in many tasks deemed traditionally as difficult (Wang et al.,
2018).

To our knowledge, there is no news reader that enables bias comparison of
articles reporting on the same topic and exploration of bias instances within
an article. More importantly, no bias-related approaches leverage most recent
advancements in NLU, which could help to significantly improve the detection
performance of biases that could not be addressed well before. We propose
_Newsalyze_ , a news reader that analyzes and visualizes WCL bias in news
articles. The prototype currently focuses on visualizing a high-level effect
of WCL bias and determines whether a target, i.e., a semantic concept, is
portrayed positively or negatively within a sentence.

##  2\. System and User’s Workflow

The system performs a five task workflow (cf. (Hamborg et al., 2019a, b)):
article gathering, preprocessing, target concept analysis, frame
identification, and visualization. For article gathering, we crawl and extract
news articles, currently for given a set of user-defined URLs (Hamborg et al.,
2017) for each topic. We then perform state-of-the-art NLP preprocessing using
Stanford CoreNLP. Target concept analysis finds and resolves semantic
concepts, such as persons or countries, across each topic’s articles, going
beyond regular coreference resolution by finding also broadly or abstractly
defined as well as contrarily mentioned concepts, such as ”freedom fighters”
vs. ”terrorists” (Hamborg et al., 2019a). Frame identification determines how
concepts are portrayed in their mentions, e.g., ranging from sentiment
polarity (positive or negative) to fine-grained framing effects, e.g., whether
a person is portrayed as being ”competent”, ”weak” or ”aggressive” (Hamborg et
al., 2019a). Identifying frames is a challenging task, for human coders (Card
et al., 2015) as well as for previous automated approaches, which either yield
mixed results if aiming to find universally valid frames (Hamborg et al.,
2019a) or are specialized to only one or a few topics (Greussing and
Boomgaarden, 2017). Thus, we currently focus on targeted sentiment, which is a
high-level effect of WCL bias but also a universal perception dimension. To
achieve state of-the-art performance in target-dependent sentiment
classification (TSC) on news articles, we use NewsTSC, a BERT-based neural
model (Hamborg, 2020).

Lastly, the system visualizes the identified instances of WCL bias using two
visualizations, which follow the overview first, details on demand mantra
(Shneiderman, 1996). First, an overview, similar to the overview offered by
news aggregators such as Google News, shows current topics and for each topic
a selection of articles reporting on it. Newsalyze’s overview enables users to
efficiently compare how articles portray the topic’s most important concepts:
besides each article snippet, the visualization shows a histogram representing
the article’s normalized sentiment of the topic’s most frequent concepts.
Figure 2 shows histograms of two articles reporting on the Iran deal topic
published by HuffPost (left-slanted outlet) and Breitbart (right) in April
2018. Second, an article-view helps users to understand WCL bias while reading
an article, e.g., by visually highlighting concept mentions as to the bias
categories identified for them on sentence-level. Figure 1 shows an excerpt of
the left-slanted article.

Figure 1. Newsalyze’s article view highlights mentions of semantic concepts,
such as persons, according to their target-dependent sentiment, a high-level
effect of bias by word choice and labeling (green: positive, red: negative).

Using the overview, users can quickly understand current topics. In contrast
to common news aggregators, the overview is bias-aware: its framing histogram
shown besides each article snippet enables users to quickly compare how
important actors are portrayed across the topic. For example, Figure 2 shows
aggregated polarities of Trump and other most frequent NEs of the Iran deal
topic. The visual comparison immediately reveals that Trump is portrayed
rather negatively in the left outlet but strongly positively in the right
outlet. In common news aggregators, users would have to read whole articles to
come to this conclusion. Lastly, the article-view aids user to understand bias
simply while reading the article, because, for example, phrases of WCL bias
are visually highlighted.

Figure 2. Framing histograms of a topic’s most frequent semantic concepts,
shown for a left-slanted (L) and a right-slanted (R) article. Each bar’s
height represents the frequency of its concept, the color aggregated positive
(green) or negative (red) sentiment of the concept.

Framing histogram of a topic’s most frequent semantic concepts.

##  3\. Conclusion and Future Work

Newsalyze is the first bias-aware news reader that supports the full news
consumption process, from getting an overview of current topics as well as
reading articles. By contrasting how a topic’s actors are portrayed by each
article, users can efficiently get an overview not only of the topic but also
of the slant of each article. Afterward, when reading an article of interest,
users are aided to see bias with the help of in-text bias markers. The system
currently analyzes and visualizes a high-level effect of bias by word choice
and labeling (WCL), i.e., target-dependent sentiment. In the future, we plan
to devise and train a neural model to additionally classify more fine-grained
perception dimensions, e.g., framing effects such as whether a person is
portrayed as competent or incompetent. We also plan to classify causes of the
identified WCL instances, e.g., the use of emotional language (see Figure 1).
We hope that in the future systems such as Newsalyze will help people to
become aware of bias conveniently during their daily news consumption. The
recently increased interest in this topic, not only in research communities
but also in society, emphasizes the issue’s importance.

###### Acknowledgements.

The work described in this paper is partially funded by the WIN program of the
Heidelberg Academy of Sciences and Humanities, financed by the Ministry of
Science, Research and the Arts of the State of Baden-Wurttemberg, Germany.

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