# Edge-enhanced Feature Distillation Network for Efficient Super-Resolution

Yan Wang
Nankai-Baidu Joint Lab, Nankai University
<EMAIL_ADDRESS>
###### Abstract

With the recently massive development in convolution neural networks, numerous
lightweight CNN-based image super-resolution methods have been proposed for
practical deployments on edge devices. However, most existing methods focus on
one specific aspect: network or loss design, which leads to the difficulty of
minimizing the model size. To address the issue, we conclude block devising,
architecture searching, and loss design to obtain a more efficient SR
structure. In this paper, we proposed an edge-enhanced feature distillation
network, named EFDN, to preserve the high-frequency information under
constrained resources. In detail, we build an edge-enhanced convolution block
based on the existing reparameterization methods. Meanwhile, we propose edge-
enhanced gradient loss to calibrate the reparameterized path training.
Experimental results show that our edge-enhanced strategies preserve the edge
and significantly improve the final restoration quality. Code is available at
https://github.com/icandle/EFDN.

##  1 Introduction

|
---|---
| HR
|
_0823_ from DIV2K | SR

|  |
---|---|---
$\mathcal{L}_{x}=0.0201$ | $\mathcal{L}_{y}=0.0092$ | $\mathcal{L}_{l}=0.0015$

Figure 1: Visual results on DIV2K [2]. The lower right parts are ground truth
gradient maps processed by the Sobel-(x,y) and Laplacian filters. The upper
left part are results for EFDN.

Image super-resolution (SR) is a widely concerning low-level computer vision
task that aims to build missing high-frequency information in degraded low-
quality images. However, it is challenging to predict the appropriate images
due to the ill-posed nature since one high-resolution (HR) image corresponds
to plenty of low-resolution images. Many classical methods [36, 30, 31] have
been proposed to address this problem, but their reconstruction quality under
large magnification is often unsatisfactory. Recently, many convolution neural
network (CNN) based approaches [9, 10, 16, 23, 38, 19, 26] were introduced to
obtain realistic super-resolution images. Notwithstanding, most of them focus
on improving restoration quality while involuntarily increasing the model
scale, resulting in difficulty for mobile devices deployment. In this paper,
we mainly concentrate on deploying SR models under resource-limited
conditions.

In order to design a qualified lightweight neural network, researchers cut
into this problem from the perspective of parameter reduction and calculation
reduction. Sticking to reducing the size of convolution and features, FSRCNN
[10] first employed the post upscaling module, which removed both calculations
and parameters. For more significant parameter reduction, the recurrent
learning is leveraged in many works, including DRCN [17] and DRRN [29].
However, these recursive approaches cost more computation resources due to
their limited representation capability. For instance, 17.9T multiply-add
operations (MAdds) are spent in DRCN and 6.8T in DRRN, which are unbearable
for mobile devices. Therefore, the researchers have shifted the critical point
of efficient SR to designing effective modules and dedicated networks.

To this end, networks [3, 15, 14] and blocks [37, 7, 6, 40] were proposed to
improve efficiency. In RFDN [24], the shallow residual block and dedicated
distillation procedure are introduced to achieve superior performance under
constrained conditions. However, its parameters and operations are still
unaffordable for part of edge devices due to the dense connections. Zhang _et
al_. [37] utilized a plain network with a re-parameterizable convolution block
to accomplish real-time application on commodity mobile devices. But they
suffer from PSNR drop like other simple networks. In addition to these
manually designed networks and blocks, many methods based on neural
architecture search (NAS) [28, 33, 5] and model pruning [20, 21] are proposed
to obtain more flexible and lightweight networks. DLSR [12] introduced a
differentiable NAS method to find a more flexible typology based on RFDN,
which designs cell-level and network-level with meager cost. Nevertheless,
DLSR overuses depth-wise convolution, bringing a large amount of activation
and memory consumption. Hence, there is still room for improvement in
designing an efficient SR model.

In order to address the above issues, we propose an efficient Edge-enhanced
Feature Distillation Network (EFDN), which combines block composing,
architecture searching, and loss designing to obtain a trade-off between the
performance and light-weighting. For block composing, we sum up the re-
parameterization methods [37, 7, 6, 8] and design a more effective and complex
edge-enhanced diverse branch block. In detail, we employ several reasonable
re-parameterizable branches to enhance the structural information extraction,
and then we integrate them into a vanilla convolution to maintain the
inference performance. To ensure the effective optimization of parallel
branches in EDBB, we design an edge-enhanced gradient-variance loss (EG) based
on gradient-variance loss [1]. The proposed loss enforces minimizing the
difference between the computed variance maps, which is helpful to restore
sharper edges. As shown in Fig. 1, we present the gradient maps calculated by
different filters and the corresponding EG loss. In addition, the NAS strategy
of DLSR is adopted to search a robust backbone.

Overall, our main contributions can be summarized as follows:

  1. 1)

We propose a plug-in edge-enhanced diverse branch block by revisiting existing
re-parameterization technologies. The block can improve the SR performance
without extra cost for inference.

  2. 2)

We design a novel gradient-variance loss function for edge information
preserve. The loss can work with the proposed EDBB to achieve higher
restoration quality.

  3. 3)

We include block composing, NAS, and loss design into our EFDN framework. And
our model achieves a competitive performance while maintaining an extremely
lightweight inference.

##  2 Related Work

###  2.1 Efficient image super-resolution

In recent years, convolutional neural networks (CNNs) have greatly promoted
the development of low-level computer vision tasks [11]. In the super-
resolution field, Dong _et al_. [9] proposed SRCNN, the earliest CNN-based
work which outperforms the traditional methods. However, SRCNN adopts post-
upscaling architecture and large convolution layers, which would result in
reduced operating efficiency. To remove the unnecessary computational cost,
the authors re-implemented the upscaling module by a deconvolution layer and
moved it to the tail part in [10]. Since then, plenty of CNN-based SR
networks[23, 38, 26, 39] has been introduced to improve the reconstruction
results. Nevertheless, most approaches leverage hundreds of convolution layers
and attention mechanisms for higher quality while ignoring the applications
under restricted resources.

To develop the efficient super-resolution in edge devices, Ahn _et al_. [3]
proposed CARN-M, a residual network with a cascading mechanism, which can
reduce parameters and computations at the expense of quality reduction. Hui
_et al_. proposed an information distillation network [15] to explicitly split
the intermediate feature to distill and compress the local long and short-path
features. Based on IDN, IMDN [14] is introduced with a more reasonable feature
distillation mechanism and effective adaptive cropping strategy. Enlightened
by revisiting these distillation mechanisms, Liu _et al_. [24] proposed a
novel channel splitting strategy that utilizes the convolution layer to
implement dimensional change. Furthermore, they devise shallow residual blocks
to improve the construction performance while maintaining the parameter scale.
With these improvements, they won first place in the AIM 2020 efficient super-
resolution challenge [35].

###  2.2 Re-parameterization

Re-parameterization has become an effective technique for efficient neural
network design. Lei _et al_. [6] proposed an asymmetric convolution block
(ACB) to strengthen the vanilla convolution by merging three different
convolutions. Then in the AIM 2020 [35], FIMDN adopted the block into the
super-resolution field and outperformed IMDN with fewer parameters. RepVGG [8]
first added identity mapping and 1$\times$1 convolution into the re-
parameterizable structure family, and DBB [7] further enriched the family with
sequential convolutions like expanding-and-squeezing convolution. Based on
these works, Zhang _et al_. [37] proposed an edge-oriented convolution block
(ECB) to improve the performance of the real-time SR network in mobile
devices.

(a) Revisiting re-parameterizable typology.

(b) Proposed edge-enhanced diverse branch block.

Figure 2: Illustration of re-parameterization method.

##  3 Proposed Method

###  3.1 Edge-enhanced diverse branch block

Figure 3: Network architecture of the proposed EFDN.

First, we revisit the existing re-parameterizable topology [8, 37, 7]. As
shown in Fig. 2(a), we present the detail of RepVGG Block, DBB, and ECB. A
total of eight different structures have been designed to improve the feature
extraction ability of the vanilla convolution in different scenarios. Although
the performance may be higher with more re-parameterizable branches, the
expensive training cost is unaffordable for straightly integrating these
paths. Meanwhile, another problem is that edge and structure information may
be attenuated during the merging of parallel branches.

To address the above concerns, we build a more delicate and effective
reparameterization block, namely Edge-enhanced Diverse Branch Block (EDBB),
which can extract and preserve high-level structural information for the low-
level task. As illustrated in Fig. 2(b), the EDBB consists of seven branches,
summarized in following two categories.

Category I: a single convolution. A normal single convolution operator can be
given as:

$\sigma(\bm{x})=\bm{W}\ast\bm{x}+\bm{b},$ (1)

Given the convolution operator ($\bm{W}$, $\bm{b}$) with a $h$$\times$$w$
kernel and the target convolution ($\bm{W}^{\rm I}$, $\bm{b}^{\rm I}$) with
the kernel shape of $H$$\times$$W$ ($h\leq H$,$w\leq W$), the process of
assigning reparameterized kernel from zero matrix can be given as:

$\displaystyle\bm{W}^{\rm
I}_{:,:,i+\lfloor\frac{H-h}{2}\rfloor,j+\lfloor\frac{W-w}{2}\rfloor}$
$\displaystyle=\bm{W}_{:,:,i,j},$ (2a) $\displaystyle\bm{b}^{\rm I}$
$\displaystyle=\bm{b},$ (2b)

While for shortcut operator, it can be treated as a special 1$\times$1
convolution, where $\bm{W}_{:,:,0,0}$ is an identity matrix.

Category II: sequential convolutions. Sequential convolutions are widely
applied to further extract the hidden information from the feature maps. In
the EDBB, we introduce expanding-and-squeezing convolution and scaled filter
convolution to enhance the edge and structure signals. Generally, these re-
parameterizable convolution sequences can be expressed by:

$\sigma_{2}(\sigma_{1}(\bm{x}))=\bm{W}_{2}\ast(\bm{W}_{1}\ast\bm{x}+\bm{b}_{1})+\bm{b}_{2},$
(3)

where the $\sigma_{1}$ is the first $D\times C\times 1\times 1$ convolution
($\bm{W}_{1}$, $\bm{b}_{1}$), and $\sigma_{2}$ is the second $C\times D\times
K\times K$ convolution ($\bm{W}_{2}$, $\bm{b}_{2}$). To merge them into a
$C\times C\times K\times K$ convolution ($\bm{W}^{\rm II}$, $\bm{b}^{\rm
II}$), we transform the formula as the form of Eq. 1. According to [37, 7], we
can obtain the target kernel in the following manner:

$\displaystyle\bm{W}^{\rm II}$ $\displaystyle=perm(\bm{W}_{1})\ast\bm{W}_{2},$
(4a) $\displaystyle\bm{b}^{\rm II}$ $\displaystyle=\bm{W}_{2}\ast
rep(\bm{b}_{1})+\bm{b}_{2},$ (4b)

where $perm$ and $rep$ are permuting and broadcasting operations to align
weights correspondingly. For $\bm{W}_{1}$, the first two dimensions are
exchanged to maintain the same size as $\bm{W}_{2}$. For $\bm{b}_{1}$, it is
replicated to share the same shape as $\bm{b}_{2}$.

In the training stage, we train the model with EDBB to obtain more reasonable
intermediate features. And then, we transfer EDBB into a vanilla convolution
by calculating the sum of re-parameterized parameters. In general, the EDBB
leads to quality promotion by utilizing more diverse re-parameterizable
branches, and it maintains the running-time inference of vanilla convolution.

###  3.2 Network architecture

Following IMDN[14] and RFDN[24], we devise an edge-enhanced information
distillation network (EFDN) to reconstruct high-quality SR images with sharp
edges and clear structure under restricted resources. As illustrated in Fig.
3, our EFDN consists of a shallow feature extraction module, multiple edge-
enhanced feature distillation blocks (EFDBs), and upscaling module.
Specifically, we leverage a single vanilla convolution to generate the initial
feature maps. Given the input LR image $I^{LR}$, this information extraction
process can be encapsulated as:

$F_{0}=E(I^{LR}),$ (5)

where the $E$ denotes the feature extraction function by a 3$\times$3
convolution, and $F_{0}$ is the extracted feature maps. This coarse feature is
then sent to stacked EFDBs for further information refining. In detail, we
replace the shallow residual block in [24] with the proposed EDBB to construct
our EFDB. Different from IMDN and RFDN utilizing dense distillation
connections to process input features progressively, we adopt network-level
NAS strategy proposed in DLSR111https://github.com/DawnHH/DLSR-PyTorch [12] to
decide the feature connection paths. The searched structure is shown in the
orange dashed box. We denote the proposed EFDB as $H_{n}$, the information
distillation procedure can be described as:

$\displaystyle F_{1}$ $\displaystyle=H_{1}(F_{0}),$ (6) $\displaystyle F_{2}$
$\displaystyle=H_{2}(F_{1}),$ $\displaystyle F_{3}$
$\displaystyle=H_{3}(C_{1}(F_{1},F_{2})),$ $\displaystyle F_{4}$
$\displaystyle=C_{3}(F_{2},H_{4}(C_{2}(F_{2},F_{3})))+F_{0},$

where $C_{n}$ is $n$-th fusion operator consisting of concatenation and
1$\times$1 convolution. $F_{n}$ is the $n$-th output feature. Finally, the SR
images are generated by upscaling module:

$I^{SR}=R(F_{4}),$ (7)

where $R$ consists of a 3$\times$3 convolution and sub-pixel operation to
convert feature maps to images.

###  3.3 Edge-enhanced gradient-variance loss

In previous work[23], $\mathcal{L}_{1}$ and $\mathcal{L}_{2}$ loss have been
in common usage to obtain higher evaluation indicators. The network trained
with these loss functions often leads to the loss of structural information.
Although the edge-oriented components are added to the EDBB, it is hard to
ensure their effectiveness during the complex training procedure of seven
parallel branches. Inspired by the gradient variance (GV) loss [1], we
proposed an edge-enhanced gradient-variance (EG) loss, which utilizes the
filters of the EDBB to monitor the optimization of the model. In detail, the
HR image $I^{HR}$ and SR image $I^{SR}$ are transferred to gray-scale images
$G^{HR}$ and $G^{SR}$. We leverage the Sobel and Laplacian filters to compute
the gradient maps and then unfold gradient maps into $\frac{HW}{n^{2}}\times
n^{2}$ patches $G_{x}$, $G_{y}$, $G_{l}$. The $i$-th variance maps can be
formulated as:

$v_{i}=\frac{\sum^{n^{2}}_{j=1}(G_{i,j}-\bar{G}_{i})^{2}}{n^{2}-1}$ (8)

where $\bar{G}_{i}$ is the mean value of the $i$-th patch. Thus, we can
calculate the variance metrics $v_{x}$,$v_{y}$,$v_{l}$ of HR and SR images,
respectively. Referring to GV-loss, we can obtain the gradient variance loss
of different filter by:

$\displaystyle\mathcal{L}_{x}$
$\displaystyle=\mathds{E}_{I^{SR}}\|v^{HR}_{x}-v^{SR}_{x}\|_{2}$ (9)
$\displaystyle\mathcal{L}_{y}$
$\displaystyle=\mathds{E}_{I^{SR}}\|v^{HR}_{y}-v^{SR}_{y}\|_{2}$
$\displaystyle\mathcal{L}_{l}$
$\displaystyle=\mathds{E}_{I^{SR}}\|v^{HR}_{l}-v^{SR}_{l}\|_{2}$

Besides, we add $\mathcal{L}_{1}$ to accelerate convergence and improve the
restoration performance. In order to better optimize the edge-oriented
branches of EDBBs and preserve sharp edges for visual effects, we set trade-
off coefficients $\lambda_{x}$, $\lambda_{y}$, and $\lambda_{l}$, which are
related to the scaled parameters of corresponding branches. In detail, we
replace the last convolution layer with EDBB during the pre-training process
to obtain the reasonable scaled parameters $s_{x}$, $s_{y}$, and $s_{l}$ of
different branches. Then, we determine the $\lambda$ by calculating the
normalized weights of $s$, and we transfer the EDBB back into a vanilla
convolution for more accessible training. The summative loss function can be
expressed by:

$\mathcal{L}_{EG}=\mathcal{L}_{1}+\lambda_{x}\mathcal{L}_{x}+\lambda_{y}\mathcal{L}_{y}+\lambda_{l}\mathcal{L}_{l}$
(10)

##  4 Experiments

###  4.1 Datasets and metrics

In the training stage, we use DIV2K [2] and Flick2K [23] (DF2K) to train our
models. A total of 3450 images are included to produce high-resolution and
bicubic down-sampled image pairs. In the evaluation stage, we utilize four
commonly used benchmark datasets: Set5 [4], Set14 [34], BSD100 [25], and
Urban100[13]. In terms of evaluation metrics, peak signal-to-noise ratio
(PSNR) and structural similarity (SSIM) [32] are calculated in the $Y$ channel
of $YCbCr$ form to validate the quality of generated images.

###  4.2 Implementation details

To obtain the LR-HR images pairs, we leverage bicubic interpolation to
downscale the 2K resolution images. We augment the training datasets by
horizontal flips and $90^{\circ}$ rotations. The HR path size and mini-batch
size are determined by the training step. The training procedure can be
summarized as follows.

  1. 1)

Pre-training the $\times$2 model on DIV2K. The LR patch size is set to
64$\times$64, and the mini-batch size is 64. $\mathcal{L}_{1}$ loss and Adam
optimizer [18] are utilized in optimization. The initial learning rate is
defined as $1\times 10^{-3}$. Referring to [40], we employ the cosine
annealing learning scheme to accelerate convergence.

  2. 2)

Training models on DF2K. In this step, The LR patch size is 64$\times$64, and
the mini-batch size is 32. We use $\mathcal{L}_{EG}$ in Eq. 10 to provide a
better visual effect. The initial learning rate is set to $5\times 10^{-4}$.

  3. 3)

Fine-tuning on DF2K. The LR patch size and mini-batch size are 120$\times$120
and 32, respectively. The $\mathcal{L}_{2}$ loss is chosen to promote the PSNR
value. The learning rate is initialized to $1\times 10^{-5}$.

  4. 4)

Reparameterizing and fine-tuning on DIV2K. The LR patch size and mini-batch
size are 160$\times$160 and 8, respectively. The $\mathcal{L}_{2}$ loss is
used, and the learning rate starts from $1\times 10^{-6}$.

The proposed method is implemented under the PyTorch framework [27] with an
NVIDIA RTX 3090 GPU.

###  4.3 Model analysis

In this subsection, we investigate model complexity including model size and
running-time, and the effectiveness of EDBB and EG loss.

Figure 4: Trade-off between performance and model complexity on Set14 [34] $\times$4 dataset. The multi-adds operation is calculated with 320$\times$180 input. Method | PSNR | Paras. | FLOPs | Act. | Mem.
---|---|---|---|---|---
IMDN [14] |  29.13/28.78 | 893K | 58.53 | 154.14 | 120
RFDN [24] | 29.04/28.75 | 433K | 27.10 | 112.03 | 200
PAN [40] | 29.01/28.70 |  272K | 32.19 | 270.53 | 311
EFDN (Ours) | 29.00/28.66 | 276K | 16.73 | 111.12 | 168
Table 1: Results on method complexity (number of parameters, FLOPs, GPU memory
consumption, number of activations).

####  4.3.1 Model compexity

In Fig. 4, we provide an overview of our Network’s deployment performance. We
can find that our EFDN obtains a better trade-off between the SR quality and
model size. To evaluate the method complexity of our EFDN precisely, we
compare several lightweight networks in Tab. 1. For fairness, the benchmark in
AIM 2020 [35] is leveraged to evaluate the inference performance. The table
shows that our EFDN achieves comparable PSNR on validation and test datasets
while consuming fewer resources. In detail, only 16.73G FLOPs are spent during
the reconstruction, which is about half of PAN [40] and 29% of IMDN [14].
Moreover, the EFDN also has the minimum activation operations and the second
least parameters and memory-consuming among these models. In terms of running
time, we compare our approach with IMDN on an NVIDIA RTX 3090 and an RTX
2070-maxQ to evaluate the efficiency in Tab. 2. Our EFDN is significantly
faster than IMDN on different GPUs.

Method | Time on 2070-maxQ | Time on 3090
---|---|---
IMDN [14] | 0.158s | 0.092s
EFDN (Ours) |  0.089s |  0.019s
Table 2: Results on running-time. Table 3: Ablation study of re-parameterizable typology and loss function on Set5 [4], Set14 [34], B100 [25], Urban100 [13] ($\times$2). We compare the our EDBB to existing blocks on FSRCNN and test different designs on VDSR. The overall improvement by EDBB and $\mathcal{L}_{EG}$ are valided in FSRCNN, VDSR, and *EFDN. The best/second-best results are highlighted and underlined. (FSRCNN and VDSR are re-implemented, and BN layers are discarded during experiments.) | Block |  3$\times$3 Conv |  1$\times$1 Conv | Identity |  Expand- Squeeze |  Scaled Filter | Set5 | Set14 | B100 | Urban100
---|---|---|---|---|---|---|---|---|---|---
FSRCNN [10] |  Baseline-$\mathcal{L}_{1}$ | ✓ |  |  |  |  | 37.09/0.9569 | 32.75/0.9098 | 31.56/0.8913 | 30.00/0.9037
Baseline-$\mathcal{L}_{EG}$ | ✓ |  |  |  |  | 37.14/0.9571 | 32.77/0.9103 | 31.58/0.8917 | 30.00/0.9040
RepVGG [8] | ✓ | ✓ | ✓ |  |  | 37.15/0.9571 | 32.78/0.9102 | 31.59/0.8916 | 30.06/0.9045
DBB [7] | ✓ | ✓ |  | ✓ | Avgpool | 37.18/0.9572 | 32.77/0.9103 | 31.60/0.8918 | 30.11/0.9050
ECB [37] | ✓ |  |  | ✓ | Laplacian & Sobel | 37.17/0.9572 |  32.80/0.9103 | 31.59/0.8915 | 30.09/0.9044
EDBB-$\mathcal{L}_{1}$ | ✓ | ✓ | ✓ | ✓ | Laplacian & Sobel |  37.19/0.9573 |  32.80/0.9104 |  31.61/0.8919 |  30.14/0.9052
EDBB-$\mathcal{L}_{EG}$ | ✓ | ✓ | ✓ | ✓ | Laplacian & Sobel |  37.27/0.9576 |  32.86/0.9109 |  31.65/0.8926 |  30.25/0.9069
VDSR [16] |  Baseline-$\mathcal{L}_{1}$ | ✓ |  |  |  |  | 37.69/0.9593 | 33.24/0.9142 | 31.99/0.8970 | 31.30/0.9198
Baseline-$\mathcal{L}_{EG}$ | ✓ |  |  |  |  | 37.72/0.9595 |  33.30/0.9147 |  32.02/0.8978 |  31.40/0.9215
EDBB-$\mathcal{L}_{1}$ | ✓ |  | ✓ | ✓ | Laplacian & Sobel | 37.73/0.9594 | 33.26/0.9143 | 31.99/0.8968 | 31.32/0.9205
EDBB-$\mathcal{L}_{1}$ | ✓ | ✓ |  | ✓ | Laplacian & Sobel |  37.73/0.9596 |  33.33/0.9145 | 32.02/0.8973 | 31.38/0.9205
EDBB-$\mathcal{L}_{1}$ | ✓ | ✓ | ✓ | ✓ | Avgpool | 37.68/0.9593 | 33.28/0.9142 | 32.00/0.8971 | 31.27/0.9190
EDBB-$\mathcal{L}_{1}$ | ✓ | ✓ | ✓ | ✓ | Laplacian & Sobel |  37.76/0.9595 |  33.33/0.9147 |  32.03/0.8975 |  31.41/0.9207
EDBB-$\mathcal{L}_{EG}$ | ✓ | ✓ | ✓ | ✓ | Laplacian & Sobel |  37.85/0.9600 |  33.41/0.9158 |  32.10/0.8987 |  31.65/0.9237
* |  Baseline-$\mathcal{L}_{1}$ | ✓ |  |  |  |  |  37.91/0.9601 |  33.44/0.9168 |  32.12/0.8990 |  31.82/0.9253
EDBB-$\mathcal{L}_{EG}$ | ✓ | ✓ | ✓ | ✓ | Laplacian & Sobel |  38.00/0.9604 |  33.57/0.9179 |  32.18/0.8998 |  32.05/0.9275
Table 4: Average PSNR/SSIM for scale $\times$2 and $\times$4 on datasets Set5 [4], Set14 [34], B100 [25], Urban100 [13] with bicubic degradation. The parameters and multi-adds are calculated with 1280$\times$720 shape output.The best/second-best results are highlighted and underlined. Dataset | Scale | Bicubic |  FSRCNN [10] |  VDSR [16] |  IDN [15] |  CARN [3] |  IMDN [14] |  PAN [40] |  EFDN (Ours)
---|---|---|---|---|---|---|---|---|---
Para/MAdds | 12K/4.6G | 665K/612.6G | 553K/31.1G | 1592K/90.9G | 715K/41.0G | 272K/28.2G | 276K/14.7G
Set5 |  $\times$2 | 33.66/0.9299 | 37.00/0.9558 | 37.53/0.9587 | 37.83/0.9600 | 37.76/0.9590 |  38.00/0.9605 |  38.00/0.9605 |  38.00/0.9604
$\times$4 | 28.42/0.8104 | 31.35/0.8838 | 31.82/0.8903 | 32.13/0.8937 |  32.21/0.8948 |  32.13/0.8948 |  32.13/0.8948 | 32.08/0.8931
Set14 |  $\times$2 | 30.24/0.8688 | 32.63/0.9088 | 33.03/0.9124 | 33.30/0.9148 | 33.52/0.9166 |  33.63/0.9177 |  33.59/0.9181 |  33.57/0.9179
$\times$4 | 26.00/0.7027 | 27.61/0.7550 | 28.01/0.7674 | 28.25/0.7730 |  28.60/0.7806 |  28.58/0.7811 |  28.60/0.7822 |  28.58/0.7809
B100 |  $\times$2 | 29.56/0.8403 | 31.53/0.8920 | 31.90/0.8960 | 32.08/0.8985 | 32.09/0.8978 |  32.19/0.8996 |  32.18/0.8997 |  32.18/0.8998
$\times$4 | 25.96/0.6675 | 26.98/0.7150 | 27.29/0.7251 | 27.41/0.7297 |  27.58/0.7349 | 27.56/0.7353 |  27.59/0.7363 |  27.56/0.7354
Urban100 |  $\times$2 | 26.88/0.8403 | 29.88/0.9020 | 30.76/0.9140 | 31.27/0.9196 | 31.92/0.9256 |  32.17/0.9283 | 32.01/0.9273 |  32.05/0.9275
$\times$4 | 23.14/0.6577 | 24.62/0.7280 | 25.18/0.7524 | 25.41/0.7632 |  26.07/0.7837 |  26.04/0.7838 |  26.11/0.7854 | 26.00/0.7815

####  4.3.2 Ablation studies of EDBB and EG loss

To evaluate the effectiveness of the proposed EDBB, we first compare it to
other re-parameterization approaches on FSRCNN [10] in Tab. 3. We can observe
that all four blocks can improve the PSNR/SSIM values, while our EDBB leads to
higher performance improvement (PSNR: +0.05$\thicksim$0.14dB, SSIM:
+0.0004$\thicksim$0.0015 on FSRCNN). We also examine the EDBB on the deeper
VDSR [16] framework by removing or replacing re-parameterizable components.
The results in Tab. 3 suggest that any branch change may lead to a quality
drop. Overall, we employ EDBB as the core feature extractor in the EFDN
backbone to improve the SR performance.

Additionally, we assess the impact of loss functions on the final resolving
quality. In detail, we leverage $\mathcal{L}_{1}$ and $\mathcal{L}_{EG}$ onto
the baseline and EDBB model, respectively. As listed in Tab. 3, the baseline
trained by $\mathcal{L}_{EG}$ brings a slight improvement on PSNR but a
performance boost on SSIM. For instance, the PSNR and SSIM of Urban100 are
developed by 0.03dB and 0.0008 on VDSR, respectively. For models equipping
EDBB, the benefits of using EG-loss are more significant. The combination
method of EDBB and $\mathcal{L}_{EG}$ surpasses baseline by a large margin
with 0.2dB improvement on three frameworks. Specifically, PSNR/SSIM results of
our EFDN increased by 0.23dB/0.002 on the Urban100 testset. Moreover, we can
infer from the advance of the SSIM index that the proposed $\mathcal{L}_{EG}$
helps the structure reconstruction by introducing edge-enhanced filters to
calibrate EDBB training.

###  4.4 Comparison with state-of-the-arts

|  |  |  |
---|---|---|---|---
| HR | Bicubic | SRCNN | FSRCNN
|  |  |  |
_291000_ from B100 [25] | VDSR | CARN | IMDN | EFDN (Ours)
|  |  |  |
| HR | Bicubic | SRCNN | FSRCNN
|  |  |  |
_img_093_ from Urban100[13] | VDSR | CARN | IMDN | EFDN (Ours)
|  |  |  |
| HR | Bicubic | SRCNN | FSRCNN
|  |  |  |
_img_099_ from Urban100 [13] | VDSR | CARN | IMDN | EFDN (Ours)
Figure 5: Qualitative results of lightweight SR models with an upscaling
factor $\times$4.

We compare our EFDN with several state-of-the-arts lightweight SR methods [9,
10, 16, 3, 15, 14, 40] on $\times$2 and $\times$4 tasks in Tab. 4. We use
PSNR/SSIM as well as the numbers of parameters and Multi-adds to show model
efficiency. It can be found that our method achieves comparable performance to
the IMDN and CARN while using fewer parameters and computations. Specifically,
the parameter number of EFDN is only 17% of CARN, and 38% of IMDN. For
$\times$4 task, the muti-adds operands used in our EFDN are far less than
these methods, which is 14.7G, about 53% in PAN and 16% in CARN. In general,
our EFDN is the most lightweight model to maintain the fidelity performance.

Apart from numerical results, we also show the visual comparison in Fig. 5.
From the patch of _291000_ , we can observe that our EFDN achieves similar
reconstruction results with these high consumption models. In _img_093_ and
_img_099_ from Urban100 [13], EFDN surpasses other methods by better quality
and less deformation on structural details such as shadows and windows.

###  4.5 Challenge results

We have participated in NTIRE 2022 Efficient Super-Resolution Challenge [22].
This competition aims to devise a practical SR method that can maintain the
PSNR value of IMDN [14] on DIV2K [2] validation with less resource
consumption. Among the final 35 valid submissions, our EFDN ranks 9th in the
running time track, 5th in the model complexity track, and 7th in the overall
performance track.

##  5 Conclusion

In this paper, we propose an edge-enhanced feature distillation network for
lightweight and accurate super-resolution. We devise an edge-enhanced diverse
branch block, which employs more effective re-parameterizable paths to achieve
better extraction capability. Furthermore, we design an edge-enhancing loss to
maximize the effectiveness of the EDBB. By introducing these strategies, the
model size is significantly and steadily reduced while maintaining a
commendable SR performance. Numerous experiments have shown the efficiency of
the proposed strengthening approaches.

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[noautoscale, width=, pages=-]images/phd_cover_hr_compressed.pdf

tocchapterTable of Contents

tocchapterList of Figures

tocchapterList of Tables

Literature Review on Object Detection,
Few-Shot Learning, and Few-Shot Object Detection

Improving Few-Shot Object Detection through Various Approaches

Rethinking Intersection Over Union

Prototyping and Industrial Application

Missing 'biblatex' package
The bibliography requires the 'biblatex' package.

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MIT Press One Rogers Street

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AcM New York, NY, USA

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family=Van DeSande,

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booktitleProceedings of the IEEE international conference on
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booktitleProceedings of the IEEE conference on computer vision and
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booktitleProceedings of the IEEE conference on computer vision and
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titleYOLOX: Exceeding YOLO Series in 2021
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booktitleProceedings of the European conference on computer vision
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booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
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booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleFcos: Fully convolutional one-stage object detection

booktitleComputer Vision–ECCV 2016: 14th European Conference,
Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14
titleSsd: Single shot multibox detector

titleVery deep convolutional networks for large-scale image
journaltitlearXiv preprint arXiv:1409.1556

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
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titleWide residual networks
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booktitleProceedings of the IEEE conference on computer vision and
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booktitleInternational conference on machine learning
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Recognition at Scale

booktitleInternational conference on machine learning
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booktitleEuropean Conference on Computer Vision
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booktitleInternational Conference on Learning Representations
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booktitleProceedings of the IEEE international conference on
computer vision
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booktitleInternational conference on machine learning
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through attention

booktitleInternational Conference on Learning Representations
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booktitleInternational Conference on Machine Learning
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inductive biases

booktitleProceedings of the IEEE/CVF international conference on
computer vision
titleSwin transformer: Hierarchical vision transformer using
shifted windows

booktitleProceedings of the IEEE/CVF conference on computer vision
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titleSwin transformer v2: Scaling up capacity and resolution

booktitleProceedings of the IEEE/CVF international conference on
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titleDINOv2: Learning Robust Visual Features without Supervision

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booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
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journaltitlearXiv preprint arXiv:2211.05778

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleDynamic Head: Unifying Object Detection Heads with

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journaltitlearXiv preprint arXiv:2211.09788

titleLearning a rotation invariant detector with rotatable
bounding box
journaltitlearXiv preprint arXiv:1711.09405

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleOriented R-CNN for object detection

booktitleProceedings of the IEEE Conference on Computer Vision and
Pattern Recognition
titleDOTA: A large-scale dataset for object detection in aerial

titleGliding vertex on the horizontal bounding box for
multi-oriented object detection
journaltitleIEEE transactions on pattern analysis and machine

titleLearning RoI transformer for detecting oriented objects in
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journaltitlearXiv preprint arXiv:1812.00155

titleGSDet: Object Detection in Aerial Images Based on Scale
journaltitleIEEE Transactions on Image Processing

titleExtended Feature Pyramid Network for Small Object Detection
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titleSniper: Efficient multi-scale training
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titleScale normalized image pyramids with AutoFocus for object
journaltitleIEEE Transactions on Pattern Analysis and Machine

titleAugmentation for small object detection
journaltitlearXiv preprint arXiv:1902.07296

titleSmall-object detection in remote sensing images with
end-to-end edge-enhanced GAN and object detector network
journaltitleRemote Sensing

titleSOD-MTGAN: Small Object Detection via Multi-Task Generative
Adversarial Network

booktitleDefense + Commercial Sensing
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Wasserstein Distance and A New Benchmark
journaltitleISPRS Journal of Photogrammetry and Remote Sensing
(ISPRS J P & RS)

booktitleProceedings of the IEEE international conference on
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booktitleProceedings of the 24th ACM international conference on
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booktitleProceedings of the AAAI conference on artificial
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journaltitleISPRS Journal of Photogrammetry and Remote Sensing

booktitleProceedings of the IEEE Conference on Computer Vision
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titleLVIS: A Dataset for Large Vocabulary Instance Segmentation

booktitleProceedings of the IEEE/CVF International Conference on
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titleObjects365: A Large-Scale, High-Quality Dataset for Object

* Invalid format of field 'month'

booktitleConference on Computer Vision and Pattern Recognition
titleAre we ready for Autonomous Driving? The KITTI Vision
Benchmark Suite

booktitleProceedings of the IEEE/CVF conference on computer vision
and pattern recognition
titleBdd100k: A diverse driving dataset for heterogeneous
multitask learning

titleCityPersons: A Diverse Dataset for Pedestrian Detection

booktitleProceedings of the IEEE/CVF winter conference on
applications of computer vision
titleScale match for tiny person detection

titleCrowdHuman: A Benchmark for Detecting Human in a Crowd
journaltitlearXiv preprint arXiv:1805.00123

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booktitleComputer Vision–ECCV 2020: 16th European Conference,
Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVI 16
titleEmbedding propagation: Smoother manifold for few-shot

booktitleProceedings of the ieee/cvf international conference on
computer vision
titleIterative label cleaning for transductive and semi-supervised
few-shot learning

titleMeta-learned confidence for few-shot learning
journaltitlearXiv preprint arXiv:2002.12017

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titleRecent advances in open set recognition: A survey
journaltitleIEEE transactions on pattern analysis and machine

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
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booktitle2018 IEEE international conference on acoustics, speech
and signal processing (ICASSP)
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data generated by generative adversarial networks

titleOpen-Set Likelihood Maximization for Few-Shot Learning
journaltitlearXiv preprint arXiv:2301.08390

titleTowards Practical Few-Shot Query Sets: Transductive Minimum
Description Length Inference
journaltitleAdvances in Neural Information Processing Systems

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleDynamic few-shot visual learning without forgetting

Taylor & Francis

titleCatastrophic forgetting, rehearsal and pseudorehearsal
journaltitleConnection Science

booktitleInternational Conference on Machine Learning
titleLearn to grow: A continual structure learning framework for
overcoming catastrophic forgetting

titleDeep visual domain adaptation: A survey

booktitleProceedings of the IEEE international conference on
computer vision
titleSimultaneous deep transfer across domains and tasks

booktitleProceedings of the IEEE international conference on
computer vision
titleFine-grained recognition in the wild: A multi-task domain
adaptation approach

booktitleProceedings of the IEEE international conference on
computer vision
titleUnified deep supervised domain adaptation and generalization

booktitlePRICAI 2014: Trends in Artificial Intelligence: 13th
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booktitleInternational conference on machine learning
titleLearning transferable features with deep adaptation networks

titleDeep domain confusion: Maximizing for domain invariance
journaltitlearXiv preprint arXiv:1412.3474

booktitleInternational conference on machine learning
titleDeep transfer learning with joint adaptation networks

titleCentral moment discrepancy (cmd) for domain-invariant
representation learning
journaltitlearXiv preprint arXiv:1702.08811

booktitleTwenty-fourth international joint conference on
artificial intelligence
titleSupervised representation learning: Transfer learning with
deep autoencoders

booktitleProceedings of the AAAI conference on artificial
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journaltitleIEEE transactions on pattern analysis and machine

titleRevisiting batch normalization for practical domain
journaltitlearXiv preprint arXiv:1603.04779

JMLR. org

titleDomain-adversarial training of neural networks
journaltitleThe journal of machine learning research

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleAdversarial discriminative domain adaptation

ACM New York, NY, USA

titleGenerative adversarial networks
journaltitleCommunications of the ACM

titleCoupled generative adversarial networks
journaltitleAdvances in neural information processing systems

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleImage-to-image translation with conditional adversarial

booktitleComputer Vision (ICCV), 2017 IEEE International
Conference on
titleUnpaired Image-to-Image Translation using Cycle-Consistent
Adversarial Networks

booktitleInternational conference on machine learning
titleCycada: Cycle-consistent adversarial domain adaptation

booktitleInternational Conference on Machine Learning
titleDeep unsupervised learning using nonequilibrium

titleDenoising diffusion probabilistic models
journaltitleAdvances in Neural Information Processing Systems

titleDiffusion-based Target Sampler for Unsupervised Domain
journaltitlearXiv preprint arXiv:2303.12724

booktitleComputer Vision–ECCV 2016: 14th European Conference,
Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14
titleDeep reconstruction-classification networks for unsupervised
domain adaptation

titleDomain separation networks
journaltitleAdvances in neural information processing systems

booktitleInternational conference on machine learning
titleLearning to discover cross-domain relations with generative
adversarial networks

titleMeta-dataset: A dataset of datasets for learning to learn
from few examples
journaltitlearXiv preprint arXiv:1903.03096

booktitleProceedings of the 29th ACM International Conference on
titleMeta-fdmixup: Cross-domain few-shot learning guided by
labeled target data

titlemixup: Beyond empirical risk minimization
journaltitlearXiv preprint arXiv:1710.09412

booktitleProceedings of the 30th ACM International Conference on
titleTGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot

booktitleProceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision
titleDomain-adaptive few-shot learning

booktitleComputer Vision–ECCV 2022: 17th European Conference, Tel
Aviv, Israel, October 23–27, 2022, Proceedings, Part XX
titleCross-Domain Cross-Set Few-Shot Learning via Learning Compact
and Aligned Representations

booktitleProceedings of the 30th ACM International Conference on
titleME-D2N: Multi-Expert Domain Decompositional Network for
Cross-Domain Few-Shot Learning

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleUniversal representation learning from multiple domains for
few-shot classification

booktitleProceedings of the 31st ACM International Conference on
Information & Knowledge Management
titleReFine: Re-randomization before Fine-tuning for Cross-domain
Few-shot Learning

titleCross-domain few-shot learning with unlabelled data
journaltitlearXiv preprint arXiv:2101.07899

titleFew-Shot Adaptation of Pre-Trained Networks for Domain Shift
journaltitlearXiv preprint arXiv:2205.15234

booktitleComputer Vision–ECCV 2022: 17th European Conference, Tel
Aviv, Israel, October 23–27, 2022, Proceedings, Part XX
titleAdversarial Feature Augmentation for Cross-domain Few-Shot

titleA Survey of Self-Supervised and Few-Shot Object Detection
journaltitlearXiv preprint arXiv:2110.14711

titleFew-Shot Object Detection: A Comprehensive Survey
journaltitleIEEE transactions on neural networks and learning

titleFew-Shot Object Detection in Remote Sensing Image
Interpretation: Opportunities and Challenges
journaltitleRemote Sensing

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleFew-shot object detection via feature reweighting

booktitleAdvances in Neural Information Processing Systems 32
titleOne-Shot Object Detection with Co-Attention and

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleMeta r-cnn: Towards general solver for instance-level
low-shot learning

titleFew-Shot Object Detection on Remote Sensing Images
journaltitleIEEE Transactions on Geoscience and Remote Sensing

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleFew-shot object detection with attention-RPN and
multi-relation detector

booktitleEuropean Conference on Computer Vision
titleFew-shot object detection and viewpoint estimation for
objects in the wild

booktitle2020 IEEE International Conference on Systems, Man, and
Cybernetics (SMC)
titleFew-Shot Object Detection via Knowledge Transfer

titleOne-shot object detection without fine-tuning
journaltitlearXiv preprint arXiv:2005.03819

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleIncremental few-shot object detection

titleFew-Shot Object Detection With Self-Adaptive Attention
Network for Remote Sensing Images
journaltitleIEEE Journal of Selected Topics in Applied Earth
Observations and Remote Sensing

Multidisciplinary Digital Publishing Institute

titleA Fast and Accurate Few-Shot Detector for Objects with Fewer
Pixels in Drone Image

booktitleProceedings of the AAAI Conference on Artificial
titleMeta faster r-cnn: Towards accurate few-shot object detection
with attentive feature alignment

titleMeta-DETR: Image-Level Few-Shot Detection with Inter-Class
Correlation Exploitation
journaltitleIEEE Transactions on Pattern Analysis and Machine

titleDynamic Relevance Learning for Few-Shot Object Detection
journaltitlearXiv preprint arXiv:2108.02235

titleDual-Awareness Attention for Few-Shot Object Detection
journaltitleIEEE Transactions on Multimedia

titleFew-Shot Object Detection via Sample Processing
journaltitleIEEE Access

booktitle2021 IEEE International Conference on Image Processing
titleJoint Co-Attention And Co-Reconstruction Representation
Learning For One-Shot Object Detection

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleTransformation invariant few-shot object detection

titleFew-Shot Object Detection on Remote Sensing Images via Shared
Attention Module and Balanced Fine-Tuning Strategy
journaltitleRemote Sensing

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleFew-shot object detection with fully cross-transformer

titleFew Shot Object Detection for SAR Images via Feature
Enhancement and Dynamic Relationship Modeling
journaltitleRemote Sensing

titleFew-shot object detection based on positive-sample
journaltitleDefence Technology

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleSemantic-aligned fusion transformer for one-shot object

booktitleProceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision
titleFew-shot object detection by attending to

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleKernelized few-shot object detection with efficient integral

titleFSODS: A Lightweight Metalearning Method for Few-Shot Object
Detection on SAR Images
journaltitleIEEE Transactions on Geoscience and Remote Sensing

titleTransformation-Invariant Network for Few-Shot Object
Detection in Remote Sensing Images
journaltitlearXiv preprint arXiv:2303.06817

booktitleProceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision
titleFew-Shot Object Detection via Improved Classification

booktitleProceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision
titlePNPDet: Efficient few-shot detection without forgetting via
plug-and-play sub-networks

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleUniversal-Prototype Enhancing for Few-Shot Object Detection

titleGendet: Meta learning to generate detectors from few shots
journaltitleIEEE Transactions on Neural Networks and Learning

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleRepmet: Representative-based metric learning for
classification and few-shot object detection

titleRestoring negative information in few-shot object detection
journaltitleAdvances in neural information processing systems

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleMorphable detector for object detection on demand

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleFSCE: Few-shot object detection via contrastive proposal

titleGeneralized and discriminative few-shot object detection via
SVD-dictionary enhancement
journaltitleAdvances in Neural Information Processing Systems

booktitleProceedings of the AAAI Conference on Artificial
titleLstd: A low-shot transfer detector for object detection

booktitleEuropean Conference on Computer Vision
titleMulti-scale positive sample refinement for few-shot object

booktitleInternational Conference on Machine Learning (ICML)
titleFrustratingly Simple Few-Shot Object Detection

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booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleGeneralized Few-Shot Object Detection without Forgetting

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleHallucination improves few-shot object detection

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleDouble Head Predictor based Few-Shot Object Detection for
Aerial Imagery

booktitleProceedings of the IEEE/CVF conference on computer vision
and pattern recognition
titleLabel, verify, correct: A simple few shot object detection

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleFew-shot object detection via classification refinement and
distractor retreatment

titleFew-Shot Object Detection via Association and DIscrimination
journaltitleAdvances in Neural Information Processing Systems

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleDefrcn: Decoupled faster r-cnn for few-shot object detection

Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and
information sciences, FOS: Computer and information sciences
titleSIMPL: Generating Synthetic Overhead Imagery to Address
Zero-shot and Few-Shot Detection Problems

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleDetreg: Unsupervised pretraining with region priors for
object detection

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleCFA: Constraint-based Finetuning Approach for Generalized
Few-Shot Object Detection

titleContext Information Refinement for Few-Shot Object Detection
in Remote Sensing Images
journaltitleRemote Sensing

booktitleICASSP 2022-2022 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP)
titleNovel instance mining with pseudo-margin evaluation for
few-shot object detection

booktitle2022 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)
titleFast Hierarchical Learning for Few-Shot Object Detection

booktitleComputer Vision–ECCV 2022: 17th European Conference, Tel
Aviv, Israel, October 23–27, 2022, Proceedings, Part IX
titleMulti-faceted Distillation of Base-Novel Commonality for
Few-Shot Object Detection

booktitleAdvances in Neural Information Processing Systems
titleDecoupling Classifier for Boosting Few-shot Object Detection
and Instance Segmentation

titleAn Effective Crop-Paste Pipeline for Few-shot Object
journaltitlearXiv preprint arXiv:2302.14452

titleIncremental-detr: Incremental few-shot object detection via
self-supervised learning
journaltitlearXiv preprint arXiv:2205.04042

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleMeta-learning to detect rare objects

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleSylph: A hypernetwork framework for incremental few-shot
object detection

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleTransductive learning for zero-shot object detection

titleMultimodal few-shot object detection with meta-learning based
cross-modal prompting
journaltitlearXiv preprint arXiv:2204.07841

booktitleProceedings of the AAAI Conference on Artificial
titleOA-FSUI2IT: A Novel Few-Shot Cross Domain Object Detection
Framework with Object-Aware Few-Shot Unsupervised Image-to-Image Translation

booktitleComputer Vision–ECCV 2022: 17th European Conference, Tel
Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII
titleAcroFOD: An Adaptive Method for Cross-Domain Few-Shot Object

booktitleProceedings of the Asian Conference on Computer Vision
titleFew-shot Adaptive Object Detection with Cross-Domain CutMix

titleCD-FSOD: A Benchmark for Cross-domain Few-shot Object
journaltitlearXiv preprint arXiv:2210.05311

booktitleComputer Vision–ECCV 2022: 17th European Conference, Tel
Aviv, Israel, October 23–27, 2022, Proceedings, Part XX
titleRethinking Few-Shot Object Detection on a Multi-Domain

titleDecoupled metric network for single-stage few-shot object
journaltitleIEEE Transactions on Cybernetics

booktitleProceedings of the IEEE/CVF International Conference on
Computer Vision
titleMeta r-cnn: Towards general solver for instance-level
low-shot learning

titleFew-Shot Object Detection with Attention-RPN and
Multi-Relation Detector

booktitleProceedings of the 28th ACM International Conference on
titleMeta-rcnn: Meta learning for few-shot object detection

titleFew-shot object detection via context-aware aggregation for
remote sensing images
journaltitleIEEE Geoscience and Remote Sensing Letters

booktitleProceedings of the European Conference on Computer Vision
titleZero-shot object detection

booktitleInternational conference on machine learning
titleLearning transferable visual models from natural language

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleFew-shot adaptive faster r-cnn

titleArthropod Taxonomy Orders Object Detection Dataset

booktitleProceedings of the 29th ACM International Conference on
titleUnderwater species detection using channel sharpening

booktitleProceedings of the European Conference on Computer Vision
titleDeep clustering for unsupervised learning of visual features

booktitleInternational workshop on similarity-based pattern
titleDeep metric learning using triplet network

titleThe mnist database of handwritten digit images for machine
learning research
journaltitleIEEE Signal Processing Magazine

titleAdam: A method for stochastic optimization
journaltitlearXiv preprint arXiv:1412.6980

titleVisualizing data using t-SNE.
journaltitleJournal of machine learning research

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleNon-local neural networks

titleLayer normalization
journaltitlearXiv preprint arXiv:1607.06450

titleWhat are diffusion models?

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titleAuto-encoding variational bayes
journaltitlearXiv preprint arXiv:1312.6114

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleHigh-resolution image synthesis with latent diffusion models

booktitleInternational Conference on Machine Learning
titleImproved denoising diffusion probabilistic models

titleDpm-solver: A fast ode solver for diffusion probabilistic
model sampling in around 10 steps
journaltitlearXiv preprint arXiv:2206.00927

titleDpm-solver++: Fast solver for guided sampling of diffusion
probabilistic models
journaltitlearXiv preprint arXiv:2211.01095

titleOn fast sampling of diffusion probabilistic models
journaltitlearXiv preprint arXiv:2106.00132

titleBlurring diffusion models
journaltitlearXiv preprint arXiv:2209.05557

titleSoft diffusion: Score matching for general corruptions
journaltitlearXiv preprint arXiv:2209.05442

titleDenoising diffusion implicit models
journaltitlearXiv preprint arXiv:2010.02502

titleCascaded Diffusion Models for High Fidelity Image
journaltitleJ. Mach. Learn. Res.

booktitleProceedings of the IEEE/CVF conference on computer vision
and pattern recognition
titleA style-based generator architecture for generative
adversarial networks

booktitleProc. CVPR
titleAnalyzing and Improving the Image Quality of StyleGAN

titleLanguage models are few-shot learners
journaltitleAdvances in neural information processing systems

titleExploring the limits of transfer learning with a unified
text-to-text transformer
journaltitleThe Journal of Machine Learning Research

booktitleInternational Conference on Machine Learning
titleZero-shot text-to-image generation

titleFlamingo: a visual language model for few-shot learning
journaltitleAdvances in Neural Information Processing Systems

titlePhotorealistic text-to-image diffusion models with deep
language understanding
journaltitleAdvances in Neural Information Processing Systems

titleSymbolic music generation with diffusion models
journaltitlearXiv preprint arXiv:2103.16091

booktitleProceedings of the AAAI Conference on Artificial
titleDiffsinger: Singing voice synthesis via shallow diffusion

booktitleProceedings of the 30th ACM International Conference on
titleProdiff: Progressive fast diffusion model for high-quality

titleLatent Diffusion for Language Generation
journaltitlearXiv preprint arXiv:2212.09462

titleGenerative time series forecasting with diffusion, denoise,
and disentanglement
journaltitleAdvances in Neural Information Processing Systems

titleDiGress: Discrete Denoising diffusion for graph generation
journaltitlearXiv preprint arXiv:2209.14734

titleUnit-ddpm: Unpaired image translation with denoising
diffusion probabilistic models
journaltitlearXiv preprint arXiv:2104.05358

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleHOLODIFFUSION: Training a 3D Diffusion Model Using 2D Images

given=Jeya MariaJose,

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleAmbiguous Medical Image Segmentation using Diffusion Models

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleCraft objects from images

booktitleProceedings of the IEEE international conference on
computer vision
titleObject detection via a multi-region and semantic
segmentation-aware cnn model

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleCascade r-cnn: Delving into high quality object detection

booktitleProceedings of the IEEE/CVF international conference on
computer vision
titleBoosting few-shot visual learning with self-supervision

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleInstance localization for self-supervised detection

titleAligning pretraining for detection via object-level
contrastive learning
journaltitleAdvances in Neural Information Processing Systems

noteVersion 1.0
titleDeep Learning Tuning Playbook

titleDetection and Tracking Meet Drones Challenge
journaltitleIEEE Transactions on Pattern Analysis and Machine

titleThe iwildcam 2021 competition dataset
journaltitlearXiv preprint arXiv:2105.03494

booktitleComputer Vision–ECCV 2020: 16th European Conference,
Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16
titleFashionpedia: Ontology, segmentation, and an attribute
localization dataset

booktitleProceedings of the IEEE/CVF conference on computer vision
and pattern recognition
titleBridging the gap between anchor-based and anchor-free
detection via adaptive training sample selection

titleGeneralized focal loss: Learning qualified and distributed
bounding boxes for dense object detection
journaltitleAdvances in Neural Information Processing Systems

booktitleProceedings of the IEEE conference on computer vision and
pattern recognition
titleImproving object localization with fitness nms and bounded
iou loss

titleA scale balanced loss for bounding box regression
journaltitleIEEE Access

booktitle2022 IEEE International Conference on Image Processing
titleHumans disagree with the IoU for measuring object detector
localization error

titleScale-sensitive IOU loss: An improved regression loss
function in remote sensing object detection
journaltitleIEEE Access

titleMeijer G–Functions: A Gentle Introduction
journaltitleNotices of the American Mathematical Society

titleYOLOv5 by Ultralytics

titleDistilling the knowledge in a neural network
journaltitlearXiv preprint arXiv:1503.02531

booktitleIn Proceedings of ICLR
titleFitNets: Hints for Thin Deep Nets

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleRelational knowledge distillation

titleKdgan: Knowledge distillation with generative adversarial
journaltitleAdvances in neural information processing systems

titleAdaptive multi-teacher multi-level knowledge distillation

booktitleProceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition
titleDistilling object detectors with fine-grained feature

booktitleProceedings of the ieee conference on computer vision and
pattern recognition
titleMimicking very efficient network for object detection

booktitleProceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision
titleData-free knowledge distillation for object detection

booktitleProceedings of the European conference on computer vision
titleQuantization mimic: Towards very tiny cnn for object

titleGan-knowledge distillation for one-stage object detection
journaltitleIEEE Access

titleKnowledge distillation: A survey
journaltitleInternational Journal of Computer Vision

titleMixed precision training
journaltitlearXiv preprint arXiv:1710.03740