275 citations · 655 across the 37 of their papers we have counts for
40 papers
SeD: Semantic-Aware Discriminator for Image Super-Resolution
Bingchen Li, Xin Li, Hanxin Zhu +4
Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular, one discriminator is utilized to enable…
Adaptive Frequency Filters As Efficient Global Token Mixers
Zhipeng Huang, Zhizheng Zhang, Cuiling Lan +3
Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable successes in broad vision tasks thanks to their effective information fusion in the global scope. Ho…
When and Why Momentum Accelerates SGD:An Empirical Study
Jingwen Fu, Bohan Wang, Huishuai Zhang +3
Momentum has become a crucial component in deep learning optimizers, necessitating a comprehensive understanding of when and why it accelerates stochastic gradient descent (SGD). T…
Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators
Zhizheng Zhang, Xiaoyi Zhang, Wenxuan Xie +1
The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a promising prospect in automatically com…
Learning Trajectories are Generalization Indicators
Jingwen Fu, Zhizheng Zhang, Dacheng Yin +2
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient des…
Mask-Based Modeling for Neural Radiance Fields
Ganlin Yang, Guoqiang Wei, Zhizheng Zhang +2
Most Neural Radiance Fields (NeRFs) exhibit limited generalization capabilities, which restrict their applicability in representing multiple scenes using a single model. To address…
Unifying Layout Generation with a Decoupled Diffusion Model
Mude Hui, Zhizheng Zhang, Xiaoyi Zhang +3
Layout generation aims to synthesize realistic graphic scenes consisting of elements with different attributes including category, size, position, and between-element relation. It…
Versatile Neural Processes for Learning Implicit Neural Representations
Zongyu Guo, Cuiling Lan, Zhizheng Zhang +2
Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Ne…
Image Coding for Machines with Omnipotent Feature Learning
Ruoyu Feng, Xin Jin, Zongyu Guo +6
Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) a…
Deep Frequency Filtering for Domain Generalization
Shiqi Lin, Zhizheng Zhang, Zhipeng Huang +9
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have unc…
Active Token Mixer
Guoqiang Wei, Zhizheng Zhang, Cuiling Lan +2
The three existing dominant network families, i.e., CNNs, Transformers, and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving design…
Mask-based Latent Reconstruction for Reinforcement Learning
Tao Yu, Zhizheng Zhang, Cuiling Lan +2
For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance. However, in practice, limited experience and h…
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Shiqi Lin, Zhizheng Zhang, Xin Li +2
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks. However, in most cases, data augmentation is randomly performed for each trainin…
Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression
Zongyu Guo, Runsen Feng, Zhizheng Zhang +2
Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussia…
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
Zhipeng Huang, Zhizheng Zhang, Cuiling Lan +6
Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. Those works assume the target domain…
MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark
Xiaotian Han, Quanzeng You, Chunyu Wang +5
Multi-camera tracking systems are gaining popularity in applications that demand high-quality tracking results, such as frictionless checkout because monocular multi-object trackin…
Confounder Identification-free Causal Visual Feature Learning
Xin Li, Zhizheng Zhang, Guoqiang Wei +4
Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free o…
Versatile Learned Video Compression
Runsen Feng, Zongyu Guo, Zhizheng Zhang +1
Learned video compression methods have demonstrated great promise in catching up with traditional video codecs in their rate-distortion (R-D) performance. However, existing learned…
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +3
Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignm…
Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Prediction
Zhizheng Zhang, Wen Song, Qiqiang Li
Remaining useful life prediction (RUL) is one of the key technologies of condition-based maintenance, which is important to maintain the reliability and safety of industrial equipm…
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Zongyu Guo, Zhizheng Zhang, Runsen Feng +1
Quantization is one of the core components in lossy image compression. For neural image compression, end-to-end optimization requires differentiable approximations of quantization,…
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +4
Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching.…
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Xin Li, Xin Jin, Tao Yu +4
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance…
Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +2
Many unsupervised domain adaptive (UDA) person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of do…