# Eliminate Deviation with Deviation for Data Augmentation and a General
Multi-modal Data Learning Method

Yunpeng Gong Liqing Huang Lifei Chen
College of Computer and Cyber Security, Fujian Normal University, P. R. China
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

One of the challenges of computer vision is that it needs to adapt to color
deviations in changeable environments. Therefore, minimizing the adverse
effects of color deviation on the prediction is one of the main goals of
vision task. Current solutions focus on using generative models to augment
training data to enhance the invariance of input variation. However, such
methods often introduce new noise, which limits the gain from generated data.
To this end, this paper proposes a strategy eliminate deviation with
deviation, which is named Random Color Dropout (RCD). Our hypothesis is that
if there are color deviation between the query image and the gallery image,
the retrieval results of some examples will be better after ignoring the color
information. Specifically, this strategy balances the weights between color
features and color-independent features in the neural network by dropouting
partial color information in the training data, so as to overcome the effect
of color devitaion. The proposed RCD can be combined with various existing
ReID models without changing the learning strategy, and can be applied to
other computer vision fields, such as object detection. Experiments on several
ReID baselines and three common large-scale datasets such as Market1501,
DukeMTMC, and MSMT17 have verified the effectiveness of this method.
Experiments on Cross-domain tests have shown that this strategy is significant
eliminating the domain gap. Furthermore, in order to understand the working
mechanism of RCD, we analyzed the effectiveness of this strategy from the
perspective of classification, which reveals that it may be better to utilize
many instead of all of color information in visual tasks with strong domain
variations.

##  1 Introduction

Figure 1: The retrieval results of the model trained with visible (RGB) image
and the model trained with grayscale image on Market1501[1] are displayed. It
shows that the color deviation between the query image and gallery image will
affect the retrieval results, and the retrieval results of some samples will
be better after ignoring the color information. The numbers on the images
indicate the rank of similarity in the retrieval results, the red and green
numbers denote the wrong and correct results, respectively.

Person re-identification (ReID) is to match the same person across diferent
cameras and scenes[2, 1, 3, 4]. This technology have been widely applied to
video surveillance[5, 6, 7], image retrieval[8, 9], criminal investigation[8],
target tracking[10] and others. The challenge of this task is that images
captured by different cameras often contain significant intra-class variation
caused by variations in viewpoint, human pose changes, occlusions, and color
deviation under variable camera conditions, etc. As a result, the appearance
of the same pedestrian image with great changes, making intra-class (the same
pedestrian) metric distance larger than inter-class (different pedestrians).
ReID usually combines representation learning[11, 12, 13, 14, 15, 16] with
metric learning[17, 18, 19, 20, 21, 22, 23], and combines classification
loss[16, 13, 11] with triplet loss[23, 22] in the training stage to optimize
the neural network. In the inference stage, it is only necessary to use Cosine
distance or Euclidean distance to measure the similarity between the query
image and the gallery image, and then rank the gallery images according to the
similarity, and finally use the re-ranking technique[24] to further refine the
search results.

The complexity of the inherent challenge of ReID means that its demand for
data has the same complexity, and the complex data demand is difficult to meet
and balance by the training set, which is also accompanied by the potential
problem that the model overfits the only training data and lacks robustness.
The dataset is hard to cover different camera environments and all their
variations at different times, so the trained models tend to overfit the given
training set and lack robustness to additional scenarios. It is no doubt that
color features are important discriminative features, but color features
instead limit the model to make correct predictions in some cases. For
example, because white and gray, black and dark blue, and brown and yellow are
similar under some lighting conditions, it is difficult for the model to make
correct predictions for negative samples that are similar to target after
overfitting the color deviation variations. As shown in (a) to (c) in Figure
1, the prediction results made by the model trained with grayscale images in
this case are better after discarding the color bias.

Realistically, color deviations cause domain gaps exist both between datasets
and within datasets[25, 26]. These color biases are practically inexhaustible.
Instead of generating a variety of data to let the model ”see” these
variations (especially intra-class variations)[25] during training to enhance
the robustness to input variations, it is better to balance the weight between
color features and other important discriminant features implicitly. Aiming at
the inherent color deviation problem that the images obtained under different
shooting conditions, this paper proposes a strategy to eliminate deviation
with deviation based on the assumption that the retrieval results of some
samples will be better when discarding color information, which is named
random color dropout (RCD). RCD balances the weight between color features and
other important discriminant features in neural network by discarding part of
the color information in the training data, so as to overcome the influence of
color deviation. This strategy exists in various forms. For example, color
deviation can be overcome with biased grayscale information or sketch
information (or contour information). Taking grayscale as an example, it can
be randomly selected a rectangular area in the RGB image and replace its
pixels with the same rectangular area in the corresponding grayscale image ,
thus it generates a training image with different areas of biases during ReID
model training. Compared with existing methods based on generative adversarial
networks (GANs)[27, 25, 26, 28, 29, 30, 31], the proposed method is more
lightweight and effective because it not only does not introduce new noise but
also saves a large amount of computational resources. At the same time, this
strategy enables the model to naturally have cross-modal retrieval[8, 9, 32,
33, 34, 35] capabilities. For example, when taking the contour information as
the intermediary to overcome the color deviation, the cross-modal retrieval
between sketch and RGB visible image can be realized.

In addition, this paper analyzes the relationship between RCD and the
generalization ability of neural networks from the perspective of
classification, and reveals the intrinsic reasons that networks trained with
RCD may outperform ordinary networks. Experiments show that the proposed
method not only increases the robustness of the model to color deviation but
also bridges the domain gap between different datasets, which has significant
advantages over the existing state-of-the-art method. Taking the grayscale as
an example, the RCD strategy proposed in this paper includes global grayscale
transformation, local grayscale transformation, and a combination of these
two. The method has the following advantages:

It is a lightweight approach which does not require any additional parameter
learning or memory consumption. It can be combined with various CNN models
without changing the learning strategy. It is a complementary approach to
existing data augmentation.

The main contributions of this paper are summarized as follows:

$\bullet$ This paper proposes a learning strategy which against color
deviation with information deviation, which decreases the overfitting and
increases generalization ability of the model.

$\bullet$ A simple and effective cross-modal retrieval method is proposed,
which does not need complex network design.

$\bullet$ This paper proves that the network trained with RCD may be better
than the ordinary network from the perspective of classification.

$\bullet$ The strategy proposed in this paper is proved to be effective in
improving ReID performance through extensive experiments and analysis. The
effectiveness of the proposed method is verified on several baselines and
representative datasets.

This work was previously published as a preprint on Arxiv and extended on the
basis of it, including related demonstration and cross-modal retrieval.

Figure 2: Framework diagram of our Random Color Dropout (RCD): The application
of global grayscale transformation and local grayscale transformation in the
framework.

##  2 Related Work

The complexity of the inherent challenge of ReID means that its demand for
data has the same complexity, and the failure to fully meet the complex demand
of the data is the source of the problem of overfitting and insufficient
generalization of the model to the training data. Improving generalization
ability is the focus of research in convolutional neural networks (CNNs).
Therefore, data augmentation is effective in improving the generalization
ability of the model.

###  2.1 Classic Data Augmentation

Many data augmentation[36] methods have been proposed, such as random
cropping[36], flipping[37], which are well known to play an important role in
classification, detection and ReID. CutMix[38] replaces one patch of an image
with a patch from another image. Random erasing or cutout[39, 40] adds noise
block to the image to regularize the network, while it helps to solve the
occlusion problem in the ReID. The above methods are regarded as indispensable
methods, and they are applied to various baselines[41, 42, 43, 44]. These
techniques have been proved to be effective in improving the prediction
accuracy, and they are complementary to each other[41].

In solving the problem of color deviation, the early work[45] used the filter
and the maximum grouping layer to learn the illumination transformation,
divided the pedestrian image into more small pieces to calculate the
similarity, and uniformly handled the problems of misalignment, occlusion and
illumination variation under the deep neural network; [46] performed pre-
processing before feature extraction and used multiscale Retinex algorithm to
enhance the color information of light shaded regions to improve the color
changes caused by lighting condition changes.

With the increasing maturity of GANs, GANs-based approaches for data
augmentation have become an active research field.

###  2.2 Data Augmentation Based on GANs

The goal of these methods is to mitigate the effect of color deviation or
human-pose variation, and to improve the robustness of the model by learning
the invariant features from the variation of the input. The appearance details
and the emphases generated by different GANs-based methods are also different,
but their goal is all to compensate for the difference between the source and
target domains. For example, CamStyle [52] generates new data for transferring
different camera styles to learn invariant features between different cameras
to increase the robustness of the model to camera style changes; CycleGAN[47]
was applied in[29, 48] to transfer pedestrian image styles from one dataset to
another; StarGAN[49] was used by[50] to generate pedestrian images with
different camera styles. Wei et al.[26] proposed PTGAN to achieve pedestrian
image transfer across different ReID datasets. It uses semantic segmentation
to extract foreground masks to assist style transfer, and converts the
background into the desired style of the dataset while keeping the foreground
unchanged. different from global style transfer, DGNet[25] utilizes GANs to
transfer clothing among different pedestrians by manipulating appearance and
structural details to generate more diverse data to reduce the impact of color
changes on the model, which effectively improves the generalization ability of
the model. In addition,[51] uses 3D engine and environment rendering
technology to build a virtual pedestrian data set with multiple lighting
conditions, which is combined with other large real data sets to jointly pre-
train a model.

The method proposed in this paper has been partially validated in other works.
[52] proved that the lack of robustness to color deviation is one of the main
reasons why the model is vulnerable to adversarial metric attacks[53, 54, 55],
and enhanced the model’s adversarial defense using the method proposed in this
paper; It is adopted in the new baseline proposed in[56, 57] to help increase
the generalization of the model. In addition, [58] showed that the method
proposed in this paper is also suitable for object detection.

##  3 Proposed Methods

The RCD strategy proposed in this paper includes global transformation, local
transformation, and a combination of the two. Taking grayscale as an example,
it includes global grayscale transformation, local grayscale transformation,
and combinations of the two. At the end of this subsection, we give the
corresponding analysis of the proposed method. The framework of this method is
showed in Figure $\color[rgb]{1,0,0}2$.

###  3.1 Global Grayscale Transformation

In the data loading, it randomly samples K identities and M images of per
person to constitute a training batch which size equals to $B=K\times M$. The
set is denoted as $x^{v}=\\{x_{i}^{v}|i=1,2,...,M\times K\\}$, where
$x_{i}^{v}=\\{x_{i}^{v}|y_{i}\\}$ represents the i-th sample image of the
training batch, and $y_{i}$ represents the class label of the pedestrian.

Taking the grayscale as an example, this method randomly performs global
grayscale transformation on the training batch with a probability, and then
inputs into the model for training. This process can be defined as:

$I_{g}=t(R,G,B)$ (1)

where $t(\bullet)$ is the grayscale image conversion function, which is
implemented by performing pixel-by-pixel accumulation calculations on the R,
G, and B channels of the original visible RGB image; y is the label of the
sample, the converted grayscale image label of $x^{g}$ are the same as the
original ones:

$(x^{g}|y)=(x^{v}|y)$ (2)

the procedure of LGT is shown in Algorithm.$\color[rgb]{1,0,0}1$.

Input : Input image $I$;

Graycale transformation probability $p$;

.

Output : Grayscale images $I^{\ast}$.

Initialization: $p_{1}\leftarrow$ Rand (0, 1).

if  _$p_{1}\geq p$_ then

$I^{\ast}\leftarrow I$;

return _$I^{\ast}$_.

else

$I^{\ast}\leftarrow$ t($I$);

return _$I^{\ast}$_.

end if

Algorithm 1 Global Graycale Transformation

###  3.2 Local Grayscale Transformation

In addition to transforming the data globally, we also consider transforming
the data locally so that the model adapt better to the significantly varying
bias due to color dropout from the local variation.

The local grayscale transformation (LGT) for each visible image $x^{v}$ can be
achieved by the following equations:

$x^{g}=t(x^{v}),$ (3)

$rect=RandPosition(x^{v}),$ (4)

$x^{lg}=LGT(x^{v},x^{g},rect)$ (5)

and

$(x^{lg}|y)=(x^{v}|y)$ (6)

where $x^{g}$ is the grayscale images, and $t(\bullet)$ is the grayscale
transformation funtion; $RandPosition(\bullet)$ is used to generate a random
rectangle in the image, and the function of $LGT(\bullet)$ is to give the
pixels in the rectangle corresponding to the $x^{g}$ image to the $x^{v}$
image; $x^{lg}$ is the sample after local grayscale transformation, and $y$ is
the label of the transformed image.

Input : Input image $I$;

Image size $W$ and $H$;

Area of image $S$;

Transformation probability $p$;

Area ratio range $s_{l}$ and $s_{h}$;

Aspect ratio range $r_{1}$ and $r_{2}$.

Output : Transformed image $I^{\ast}$.

Initialization: $p_{1}\leftarrow$ Rand (0, 1).

if  _$p_{1}\geq p$_ then

$I^{\ast}\leftarrow I$;

return _$I^{\ast}$_.

else

while  _True_ do

$S_{t}\leftarrow$ Rand $(s_{l},s_{h})$$\times S$;

$r_{t}\leftarrow$ Rand $(r_{1},r_{2})$;

$H_{t}\leftarrow\sqrt{S_{t}\times r_{t}}$,
$W_{t}\leftarrow\sqrt{\frac{S_{t}}{r_{t}}}$;

$x_{t}\leftarrow$ Rand $(0,W)$, $y_{t}\leftarrow$ Rand $(0,H)$;

if  _$x_{t}+W_{t}\leq W$ and $y_{t}+H_{t}\leq H$_ then

$Position\leftarrow(x_{t},y_{t},x_{t}+W_{t},y_{t}+H_{t})$;

$I(Position)\leftarrow$ $t(Position)$;

$I^{\ast}\leftarrow I$;

return _$I^{\ast}$_.

end if

end while

end if

Algorithm 2 Local Graycale Transformation

In the process of model training, we conduct LGT randomly transformation on
the training batch with a probability. For an image $I$ in a batch, denote the
probability of it undergoing LGT be $p_{r}$, and the probability of it being
kept unchanged be $1-p_{r}$. In this process, it randomly selects a
rectangular region in the image and replaces it with the pixels of the same
rectangular region in the corresponding grayscale image. Thus, training images
which include regions with different levels of grayscale are generated. Among
them, $s_{l}$ and $s_{h}$ are the minimum and maximum values of the ratio of
the image to the randomly generated rectangle area, and the $S_{t}$ of the
rectangle area limited between the minimum and maximum ratio is obtained by
$S_{t}$ ← $Rand(s_{l},s_{h})\times S$, $r_{t}$ is a coefficient used to
determine the shape of the rectangle. It is limited to the interval ($r_{1}$,
$r_{2}$ ). $x_{t}$ and $y_{t}$ are randomly generated by coordinates of the
upper left corner of the rectangle. If the coordinates of the rectangle exceed
the scope of the image, the area and position coordinates of the rectangle are
re-determined. When a rectangle that meets the above requirements is found,
the pixel values of the selected region are replaced by the corresponding
rectangular region on the grayscale image converted from RGB image. As a
result, training images which include regions with different levels of
grayscale are generated, and the object structure is not damaged. The
procedure of LGT is shown in Algorithm.$\color[rgb]{1,0,0}2$.

###  3.3 Loss function

ReD usually combines classification loss and triplet loss to train the
model[59, 60]. Here, we use $x_{i}^{v}$ to denote the $i$-th RGB image in a
training batch, and $x_{i}^{g}$ to denote the image obtained after GGT or LGT
conversion. Thenthe features of $x_{i}^{v}$ and $x_{i}^{g}$ can be expressed
as:

$\left\\{\begin{array}[]{ll}f_{i}^{v}=f(x_{i}^{v})\\\
f_{i}^{g}=f(f_{i}^{g})\end{array}\right.$ (7)

The Euclidean distance between two samples $x_{i}^{g}$ and $x_{j}^{g}$ is
denoted as $D(x_{i}^{g},x_{j}^{g})$, where The subscript $\\{i,j\\}$ denotes
the image index in the training batch. Formally, let $x_{i}^{g}$ be the anchor
sample, the triple $\\{x_{i}^{g},x_{j}^{g},x_{k}^{g}\\}$ is selected in the
following way:

$P_{i,j}^{g}=\max_{\forall y_{i}=y_{j}}D(x_{i}^{g},x_{j}^{g})$ (8)
$N_{i,k}^{g}=\min_{\forall y_{i}\neq y_{j}}D(x_{i}^{g},x_{k}^{g})$ (9)

For each anchor point $x_{i}^{g}$, the above strategy selects the positive
sample pair with the same pedestrian class label and the farthest the most
distant positive sample pair with the same pedestrian category label and the
nearest negative sample pairs, forming a triplet
$\\{x_{i}^{g},x_{j}^{g^{+}},x_{k}^{g^{-}}\\}$ for mining grayscale
information. In general, the use of The boundary parameter $\varepsilon$ is
used to control the spacing of the positive and negative sample pairs. In
summary, we can define the following triadic loss for training:

$L_{g}=\frac{1}{n}\sum_{i=1}^{n}\max[\varepsilon+D(x_{i}^{g},x_{j}^{g^{+}})+D(x_{i}^{g},x_{k}^{g^{-}})]$
(10)

In addition, $x^{v}$ and $x^{g}$ are trained using a shared identity
classifier $\phi$. The predicted probability of identity labels $y_{i}$ is
define as $p(y_{i}|x_{i}^{g};\phi)$. The ID loss is represented as follows:

$L_{ide}=-\frac{1}{n}\sum_{i=1}^{n}log(p(y_{i}|x_{i}^{g};\phi))$ (11)

Therefore, the overall loss during random grayscale transformation is:

$L_{total}=L_{g}+L_{ide}$ (12)

###  3.4 Analysis of Random Color Dropping Policy

Here suppose there are $m$ instances, the expected output, i.e.
$D=[d_{1},d_{2},…,d_{m}]^{T}$ where $d_{j}$ denotes the expected output on the
$j$-th instance, and the actual output of the $i$-th component neural network,
i.e. $F_{i}=[f_{i1},f_{i2},…,f_{im}]^{T}$ where $f_{ij}$ denotes the actual
output of the $i$-th component network on the $j$-th instance. $D$ and $F_{i}$
satisfy that $d_{j}$ $\in\\{-1,+1\\}(j=1,2,…,m)$ and
$f_{ij}$$\in\\{-1,+1\\}(i=1,2,…,N;j=1,2,…,m)$ respectively. It is obvious that
if the actual output of the $i$-th component network on the $j$-th instance is
correct according to the expected output then $f_{ij}d_{j}=+1$, otherwise
$f_{ij}d_{j}=-1$. Thus the generalization error of the $i$-th component neural
network on those $m$ instances is:

$E_{i}=\frac{1}{m}\sum_{j=1}^{m}{Error(f_{ij}d_{j})}$ (13)

where $Error(x)$ is a function defined as:

$Error(x)=\left\\{\begin{array}[]{ll}1,\quad\quad if\quad x=-1\\\ 0.5,\quad
if\quad x=0\\\ 0,\quad\quad if\quad x=1\end{array}\right.$ (14)

Here we introduce a vector $Sum=[Sum_{1},Sum_{2},…,Sum_{m}]^{T}$ where
$Sum_{j}$ denotes the sum of the actual output of all the component neural
networks on the $j$-th instance, i.e.

$Sum_{j}=\sum_{i=1}^{N}f_{ij}$ (15)

Then the output of the neural network ensemble on the j-th instance is:

$\hat{f_{j}}=Sgn(Sum_{j})$ (16)

where $Sgn(x)$ is a function defined as:

$Sgn(x)=\left\\{\begin{array}[]{ll}1,\quad\quad if\quad x>0\\\ 0,\quad\quad
if\quad x=0\\\ -1,\quad if\quad x<0\end{array}\right.$ (17)

It is obvious that $\hat{f}_{j}\in\\{-1,0,+1\\}(j=1,2,…,m)$ . If the actual
output of the ensemble on the $j$-th instance is correct according to the
expected output then $\hat{f_{j}}d_{j}=+1$; if it is wrong then
$\hat{f}_{j}d_{j}=-1$; otherwise $\hat{f}_{j}d_{j}=0$, which means that there
is a tie on the j-th instance, e.g. three component networks vote for +1 while
other three networks vote for -1. Thus the generalization error of the
ensemble is:

$\hat{E}=\frac{1}{m}\sum_{j=1}^{m}{Error(\hat{f_{j}}d_{j})}$ (18)

Here suppose that the k-th component neural network is trained using grayscale
images. Then the output of the new set on the $j$-th instance is:

$\hat{f_{j}^{{}^{\prime}}}=Sgn(Sum_{j(j\neq k)}-f_{kj})$ (19)

and the generalization error of the new ensemble is:

$\hat{E^{{}^{\prime}}}=\frac{1}{m}\sum_{j=1}^{m}{Error(\hat{f_{j}^{{}^{\prime}}}d_{j})}$
(20)

It is assumed that a certain number of networks with deviations will not
affect the performance of the overall neural network, and the retrieval
results of some examples will be better after ignoring the color information.

$Error(\hat{f_{j}^{{}^{\prime}}}d_{j})\leqslant Error(\hat{f_{j}}d_{j})$ (21)

From Eq.$\color[rgb]{1,0,0}(18)$ and Eq.$\color[rgb]{1,0,0}(20)$ we can derive
that if Eq.$\color[rgb]{1,0,0}(21)$ is satisfied then $\hat{E}$ is not smaller
than $\hat{E^{{}^{\prime}}}$, which means that the ensemble including k-th
component neural network which is trained using grayscale images is better
than the one no including:

$\begin{split}\sum_{j=1}^{m}\\{Error(Sgn(Sum_{j})d_{j})-\\\
Error(Sgn(Sum_{j(j\neq k)+f{kj}})d_{j})\\}\geq 0\end{split}$ (22)

##  4 Comparison and Analysis

Figure 3: Performance of GGT under different hyperparameters on Market1501.

###  4.1 Datasets and Evaluation criteria

Datasets. Market-1501 [1] includes 1,501 pedestrians captured by six cameras
(five HD cameras and one low-definition camera). DukeMTMC [61] is a large-
scale multi-target, multi-camera tracking dataset, a HD video dataset recorded
by 8 synchronous cameras, with more than 2,700 individual pedestrians. The
above two datasets are widely used in ReID studies. MSMT17[26], created in
winter, was presented in 2018 as a new, larger dataset closer to real-life
scenes, containing a total of 4,101 individuals and covering multiple scenes
and time periods.

These three datasets are currently the largest datasets of ReID, and they are
also the most representative because they collectively contain multi-season,
multi-time, HD, and low-definition cameras with rich scenes and backgrounds as
well as complex lighting variations.

Sketch ReID dataset[8] contains 200 persons, each of which has one sketch and
two photos. Photos of each person were captured during daytime by two cross-
view cameras. It cropped the raw images (or video frames) manually to make
sure that every photo contains the one specific person. It have a total of 5
artists to draw all persons’sketches and every artist has his own painting
style.

Evaluation criteria. Following existing works [1], Rank-k precision and mean
Average Precision (mAP) are adapted as evaluation metrics. Rank-1 denotes the
average accuracy of the first return result corresponding to each query image.
mAP denotes the mean of average accuracy, the query results are sorted
according to the similarity, the closer the correct result is to the top of
the list, the higher the score.

Figure 4: Performance of LGT under different hyperparameters on Market1501.

###  4.2 Hyper-Parameter Setting

During CNN training, two hyper-parameters need to be evaluated. One of them is
GGT probability $p$. Firstly, we take the hyper-parameter $p$ as 0.01, 0.03,
0.05, 0.07, 0.1, 0.2, 0.3,…, 1 for the GGT experiments. Then we take the value
of each parameter for three independent repetitions of the experiments.
Finally, we calculate the average of the final result. The results of
different p are shown in Fig.$\color[rgb]{1,0,0}3$. We can see that when
$p=0.05$, the performance of the model reaches the maximum value in Rank-1 and
mAP. If we do not specify, the hyper- parameter is set $p=0.05$ in the next
experiments.

Another hyper-parameter is LGT probability $p_{r}$. We take the hyper-
parameter $p_{r}$ as the same as above for the LGT experiments, whose
selection process is similar to the above $p$. The results of different
$p_{r}$ are shown in Fig.$\color[rgb]{1,0,0}4$.

Obviously, when $p_{r}=0.4$ or $p_{r}=0.7$, the model achieves better
performance. And the best performance is achieved when $p_{r}=0.4$. If we do
not specify, the hyper- parameter is set $p_{r}=0.4$ in the subsequent
experiments.

Figure 5: Performance of combining GGT with LGT under different
hyperparameters on Market1501.

Evaluation of GGT and LGT. Compared with the best results of GGT on
baseline[44], the accuracy of LGT is improved by 0.5% and 1.4% on Rank-1 and
mAP, respectively. Under the same conditions using re-Ranking[24], the
accuracy of Rank-1 and mAP is improved by 0% and 0.4%, respectively.
Therefore, the advantages of LGT are more obvious when re-Ranking is not used.
However, Fig.$\color[rgb]{1,0,0}4$ also shows that the performance improvement
brought by LGT is not stable enough because of the obvious fluctuation in LGT,
while the performance improvement brought by GGT is very stable. Therefore, we
improve the stability of the method by combining GGT with LGT.

Evaluation by Combining GGT with LGT. First, we fix the hyper-parameter value
of GGT to $p=0.05$, then keep the control variable unchanged to further
determine the hyper-parameter of LGT. Finally, we take the hyper-parameter pr
of LGT to be 0.1, 0.2, ···, 0.7 to conduct combination experiments of GGT and
LGT, and conduct 3 independent repeated experiments for each parameter $p_{r}$
to get the average value. The result is shown in $\color[rgb]{1,0,0}5$. It can
be seen that the performance improvement brought by the combination of GGT and
LGT is more stable and with less fluctuation, and the comprehensive
performance of the model is the best when the hyper-parameter value of LGT is
$p_{r}=0.4$.

Figure 6: Diagram of Global Sketch Transformation (GST) and Local Sketch
Transformation (LST).

###  4.3 Comparison Experiments

Performance comparison and analysis. We first evaluate baseline [44] on the
Market-1501 dataset[1]. To be consistent with recent works, we follow the new
training/testing protocol to conduct our experiments by k-reciprocal re-
ranking (RK) [24]. As can be seen from Fig.$\color[rgb]{1,0,0}3$ and
Fig.$\color[rgb]{1,0,0}4$, our method improves by 1.2% on Rank-1 and 3.3% on
mAP on the baseline, and 1.5% on Rank-1 and 2.1% on mAP above baseline in the
same conditions using the re-Ranking[24].

Secondly, we further test the method in this paper on the baselines[59, 60]
with better performance. As we can see from Table.$\color[rgb]{1,0,0}1$ to
Table.$\color[rgb]{1,0,0}3$, the best results of our method improve by 0.6%
and 1.3% on the Rank-1 and mAP on the strong baseline[59], respectively, and
0.8% and 0.5% Rank-1 and mAP above baseline under the same conditions using
the re-Ranking[24], respectively. On fastReID[60], our method is 0.2% higher
and 0.9% than baseline in Rank-1 and mAP, respectively, and higher 0.1% and
0.3% than baseline under using re-Ranking.

The default configuration on the Strong Baseline[59] and FastReID[60] uses
data augmentation such as random flipping[37], cropping[36], and erasing[39].
The method proposed in this paper further improves the model accuracy on the
basis of using them, which shows that our method can be combined with other
data augmentation methods.

Table 1: Performance comparison on Market1501 dataset. Methods | Market1501
---|---
Rank-1(%) | mAP(%)
IANet[62] | 94.4 | 83.1
DGNet[25] | 94.8 | 86.0
SCAL[63] | 95.8 | 89.3
Circle Loss[64] | 96.1 | 87.4
SB[59] | 94.5 | 85.9
SB[59] \+ RK[24] | 95.4 | 94.2
SB + GGT(ours) | 94.6 | 85.7
SB + GGT+ RK(ours) | 96.2 | 94.7
SB + LGT(ours) | 95.1 | 87.2
SB + LGT + RK(ours) | 95.9 | 94.4
FastReID[60] | 96\. 3 | 90.3
FastReID + RK | 96.8 | 95.3
FastReID + GGT(ours) | 96.5 | 91.2
FastReID + GGT + RK(ours) | 96.9 | 95.6
Table 2: Performance comparison on DukeMTMC dataset. Methods | DukeMTMC
---|---
Rank-1(%) | mAP(%)
IANet[62] | 87.1 | 73.4
DGNet[25] | 86.6 | 74.8
SCAL[63] | 89.0 | 79.6
SB[59] | 86.4 | 76.4
SB + RK[24] | 90.3 | 89.1
SB + GGT(ours) | 87.8 | 77.3
SB + GGT+ RK(ours) | 90.9 | 89.2
SB + LGT(ours) | 87.3 | 77.3
SB + LGT + RK(ours) | 91 | 89.4
FastReID[60] | 92.4 | 83.2
FastReID + RK | 94.4 | 92.2
FastReID + LGT(ours) | 92.8 | 84.2
FastReID + LGT + RK(ours) | 94.3 | 92.7
Table 3: Performance comparison on MSMT17 dataset. Methods | MSMT17
---|---
Rank-1(%) | mAP(%)
IANet[62] | 75.5 | 46.8
DGNet[25] | 77.2 | 52.3
RGA-SC[65] | 80.3 | 57.5
SCSN[66] | 83.8 | 58.5
AdaptiveReID[67] | 81.7 | 62.2
FastReID[60] | 85.1 | 63.3
FastReID + GGT(ours) | 86.2 | 65.3
FastReID + GGT&LGT(ours) | 86.2 | 65.9

Cross-domain tests. Cross-domain person re-identification aims at adapting the
model trained on a labeled source domain dataset to another target domain
dataset without any annotation. It is pointed out by[59] that the higher
accuracy of the model does not mean that it has better generalization
capacity. In response to the above potential problems, we use cross-domain
tests to verify the robustness of the model. Experiments show that the
proposed method effectively enhances the generalization capacity of the model,
and the Table $\color[rgb]{1,0,0}2$ shows the cross-domain experiments of the
proposed method between two datasets, Market-1501[1] and DukeMTMC[61]. In
order to further explore the effectiveness of the proposed method in cross-
domain experiments, we use GGT to conduct the following cross-domain
experiments on strong baseline[59]. The experiments are shown in
Table.$\color[rgb]{1,0,0}4$.

In Table 4, +REA means that the trick of Random Erasing is used in model
training, -REA means turning it off. Experimental results show that random
erasing[39] can also significantly improve the performance of the ReID model,
but it will cause a significant drop in cross-domain performance. The proposed
method can not only significantly improve the cross-domain performance of the
ReID model, but also be more robust because of learning more discriminative
features.

Table 4: The performance of different models is evaluated on cross-domain dataset. M→D means that we train the model on Market1501[1] and evaluate it on DukeMTMC[61]. Methods | Cross-Domain
---|---
M→D | D→M
Rank-1/mAP(%) | Rank-1/mAP(%)
SB[59]+REA[39]+RK | 33.6/24.3 | 51.6/32.3
SB+REA+GGT+RK(ours) | 37.8/27.8 | 55.4/35.7
SB-REA+RK | 45.5/37.0 | 58.2/37.8
SB-REA+GGT+RK(ours) | 48.2/37.9 | 65.0/43.7
Table 5: Performance comparison between our LGT conversion and DGNet data augmentation on Market1501. Methods | Market1501
---|---
Rank-1 | mAP(%)
Baseline[44] | 88.8 | 71.6
Baseline + DGNet[25] | 88.9 | 72.1
Baseline+ LGT(ours) | 90.0 | 74.9
Table 6: Cross-domain performance comparison between our LGT and DGNet on Market1501. Methods | Market1501→DukeMTMC
---|---
Rank-1 | mAP
Baseline[44] | 37.8 | 27.0
Baseline + DGNet[25] | 36.7 | 25.6
Baseline+ LGT(ours) | 39.7 | 27.9
Table 7: Performance comparison on Market1501 dataset. Methods | Market1501
---|---
Rank-1(%) | mAP(%)
Baseline[44] | 88.8 | 71.6
Baseline + RK[24] | 90.5 | 85.2
Baseline+ GST+LST(ours) | 88.9 | 72.6
Baseline+ GST+LST+RK(ours) | 91.2 | 86.8
Table 8: Performance comparison between our RCD and Adversarial Feature Learning on Sketch ReID dataset. Sketch ReID dataset | Rnak-1(%) | Rnak-5(%) | Rank-10(%)
---|---|---|---
AFL[8] | 34.0 | 56.3 | 72.5
GST+LST(ours) | 42.5 | 70.0 | 87.5
Figure 7: t-SNE [68] visualization of six randomly selected images with
different identities on Market1501[1]. Each image corresponds to the randomly
generated images with color deviation. The same color means that they are
obtained by transformation of the same image. Dots means original example.

Comparison of state-of-the-arts. A comparison of the performance of our method
with the state-of-the-art methods DGNet[25] on Market1501[1] is shown in
Table.$\color[rgb]{1,0,0}5$ and Table.$\color[rgb]{1,0,0}6$. As can be seen
from Table.$\color[rgb]{1,0,0}5$, our method delivers a performance
improvement that far exceeds that of DGNet, the state-of-the-art GAN-based
method, by more than 2.7 percentage points on mAP, which suggests that the
proposed method is superior to existing data augmentation.

As can be seen from Table.$\color[rgb]{1,0,0}6$, the generalization ability of
the proposed method in cross-domain tests is improved by 1.9 percentage points
in the Rank-1 compared with the baseline[44], which further shows that the
proposed method is better than the existing data augmentation based on
generative models. It is worth noting that when the data generated by DGNet is
used for model training, the cross-domain performance of the model is poorly,
which confirms the point of this paper that color deviation is is difficult to
exhaust and that instead of enhancing robustness to input changes by
generating a variety of data for the model to ”see” during training, it is
better to implicitly reduce the weight of the model in the discriminant
feature of color information.

Cross-modal retrival. Another form of strategy proposed in this paper is to
take sketch image as the intermediary of balancing weight. By applying the
proposed global homogeneity transformation and local homogeneity
transformation, the sketch image is transformed as a homogeneous image, as
shown in Figure.$\color[rgb]{1,0,0}6$. It can not only improve the robustness
of the model, but also realize the sketch-based ReID. We can see this clearly
from Table.$\color[rgb]{1,0,0}.7$ and Table.$\color[rgb]{1,0,0}.8$.

In terms of cross-modal retrieval[69, 8, 70], in order to match images of
different modalities, existing approaches usually achieve cross-modal
retrieval with the help of attention mechanisms[69], multi-stream
networks[70], and generative adversarial networks[8]. Lu et al. proposed a
cross-domain adversarial feature learning (AFL) method for sketch re-
identification and contributed the sketch character re-identification
dataset[8].

In order to make a fair comparison, as same as AFL[8], the method proposed in
this paper is firstly trained on the Market-1501 dataset, and then fine tuned
on sketch ReID dataset. In parameter setting, this paper set 5% Global Sketch
Transformation and 70% Local Sketch Transformation. The experiment result
shows that the performance improvement in the Sketch Re-identification more
than 8%. This experiment also shows the generality of the proposed method.

Figure 8: Comparison of Grad-CAM [71] activation map between normally trained
model and our proactive defense model.

Visualization analysis. As the show in Figure $\color[rgb]{1,0,0}7$, the model
trained by DCR is robust to color variations. Therefore, we can observe that
the features of examples with color deviation exhibit clustering effects
better.

Grad-CAM [71] uses the gradient information flowing into the last
convolutional layer of the CNN to visualize the importance of each neuron in
the output layer for the final prediction, by which it is possible to
visualize which regions of the image have a significant impact on the
prediction of a model. As shown in Figure $\color[rgb]{1,0,0}8$, we can see
that the the normally trained model activates irrelevant parts in the case of
severe color deviation, while the model trianed with RCD is still effectively
activating some important parts.

##  5 Conclusion

In this paper, a simple, effective and general strategy that can be applied in
computer vision to overcome color deviation. Neither does the method require
large scale training like GAN, nor introduces any noise. The method uses a
random homogeneous transformation to realize the modeling of different modal
relationships. The model balances the weights between color features and
discriminative non-color features by fitting differentiated homogeneous
information in a mixed domain with information bias during the training
process, thus reducing the negative impact of color deviation on ReID. In
addition, this paper reveals the intrinsic reasons why networks trained with
RCD outperform ordinary networks from a classification perspective. At the
same time, experiments on several datasets and baselines show that the
proposed method is effective and outperforms the state of the arts, and extra
experiments show that the proposed strategy has natural cross modal
properties.

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