67 citations · 969 across the 81 of their papers we have counts for
83 papers
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Xin Li +49
This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compress…
Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks
Gen Luo, Yiyi Zhou, Xiaoshuai Sun +5
Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to it…
Differentiated Relevances Embedding for Group-based Referring Expression Comprehension
Fuhai Chen, Xuri Ge, Xiaoshuai Sun +4
The key of referring expression comprehension lies in capturing the cross-modal visual-linguistic relevance. Existing works typically model the cross-modal relevance in each image,…
ASFD: Automatic and Scalable Face Detector
Jian Li, Bin Zhang, Yabiao Wang +6
Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However,…
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context
Yuxi Li, Boshen Zhang, Jian Li +5
In this paper, we place the atomic action detection problem into a Long-Short Term Context (LSTC) to analyze how the temporal reliance among video signals affect the action detecti…
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Jiawei Zhao, Ke Yan, Yifan Zhao +3
The simultaneous recognition of multiple objects in one image remains a challenging task, spanning multiple events in the recognition field such as various object scales, inconsist…
Spatiotemporal Inconsistency Learning for DeepFake Video Detection
Zhihao Gu, Yang Chen, Taiping Yao +4
The rapid development of facial manipulation techniques has aroused public concerns in recent years. Following the success of deep learning, existing methods always formulate DeepF…
Towards Language-guided Visual Recognition via Dynamic Convolutions
Gen Luo, Yiyi Zhou, Xiaoshuai Sun +3
In this paper, we are committed to establishing an unified and end-to-end multi-modal network via exploring the language-guided visual recognition. To approach this target, we firs…
Fine-grained Data Distribution Alignment for Post-Training Quantization
Yunshan Zhong, Mingbao Lin, Mengzhao Chen +5
While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce imag…
Distributed Attention for Grounded Image Captioning
Nenglun Chen, Xingjia Pan, Runnan Chen +7
We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image w…
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
Qingyu Song, Changan Wang, Zhengkai Jiang +6
Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization…
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing
Shubao Liu, Ke-Yue Zhang, Taiping Yao +5
With various face presentation attacks arising under unseen scenarios, face anti-spoofing (FAS) based on domain generalization (DG) has drawn growing attention due to its robustnes…
Discriminator-Free Generative Adversarial Attack
Shaohao Lu, Yuqiao Xian, Ke Yan +5
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of t…
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
Yuhan Wang, Xu Chen, Junwei Zhu +7
In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unli…
Adaptive Feature Alignment for Adversarial Training
Tao Wang, Ruixin Zhang, Xingyu Chen +6
Recent studies reveal that Convolutional Neural Networks (CNNs) are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. Many advers…
Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection
Zhenyu Zhang, Yanhao Ge, Renwang Chen +6
Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on…
Consistent Instance False Positive Improves Fairness in Face Recognition
Xingkun Xu, Yuge Huang, Pengcheng Shen +5
Demographic bias is a significant challenge in practical face recognition systems. Existing methods heavily rely on accurate demographic annotations. However, such annotations are…
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing
Zhihong Chen, Taiping Yao, Kekai Sheng +5
Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. Existing DG methods assume that the do-main la…
ISTR: End-to-End Instance Segmentation with Transformers
Jie Hu, Liujuan Cao, Yao Lu +6
End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replac…
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Jiangning Zhang, Chao Xu, Jian Li +7
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them hav…
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Yixu Wang, Jie Li, Hong Liu +4
Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we obse…
Delving into Data: Effectively Substitute Training for Black-box Attack
Wenxuan Wang, Bangjie Yin, Taiping Yao +6
Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, t…
Carrying out CNN Channel Pruning in a White Box
Yuxin Zhang, Mingbao Lin, Chia-Wen Lin +5
Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which…
DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning
Yuting Gao, Jia-Xin Zhuang, Shaohui Lin +4
While self-supervised representation learning (SSL) has received widespread attention from the community, recent research argue that its performance will suffer a cliff fall when t…