71 citations · 130 across the 12 of their papers we have counts for
12 papers
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
Jiahang Zhang, Lilang Lin, Shuai Yang +1
Self-supervised learning (SSL), which aims to learn meaningful prior representations from unlabeled data, has been proven effective for skeleton-based action understanding. Differe…
Zero-Reference Low-Light Enhancement via Physical Quadruple Priors
Wenjing Wang, Huan Yang, Jianlong Fu +1
Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during…
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning
Jiahang Zhang, Lilang Lin, Jiaying Liu
Self-supervised learning has proved effective for skeleton-based human action understanding, which is an important yet challenging topic. Previous works mainly rely on contrastive…
Similarity Min-Max: Zero-Shot Day-Night Domain Adaptation
Rundong Luo, Wenjing Wang, Wenhan Yang +1
Low-light conditions not only hamper human visual experience but also degrade the model's performance on downstream vision tasks. While existing works make remarkable progress on d…
Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation
Wenjing Wang, Huan Yang, Zixi Tuo +4
With the explosive popularity of AI-generated content (AIGC), video generation has recently received a lot of attention. Generating videos guided by text instructions poses signifi…
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition
Lilang Lin, Jiahang Zhang, Jiaying Liu
The self-supervised pretraining paradigm has achieved great success in skeleton-based action recognition. However, these methods treat the motion and static parts equally, and lack…
Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation
Yiyang Ma, Huan Yang, Wenjing Wang +2
Language-guided image generation has achieved great success nowadays by using diffusion models. However, texts can be less detailed to describe highly-specific subjects such as a p…
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations
Jiahang Zhang, Lilang Lin, Jiaying Liu
Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to…
Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation
Wenjing Wang, Zhengbo Xu, Haofeng Huang +1
Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light…
AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation
Yiyang Ma, Huan Yang, Bei Liu +2
AI illustrator aims to automatically design visually appealing images for books to provoke rich thoughts and emotions. To achieve this goal, we propose a framework for translating…
SMars: Semi-Supervised Learning for Mars Semantic Segmentation
Jiahang Zhang, Lilang Lin, Zejia Fan +2
Deep learning has become a powerful tool for Mars exploration. Mars terrain semantic segmentation is an important Martian vision task, which is the base of rover autonomous plannin…
On the Connection between Local Attention and Dynamic Depth-wise Convolution
Qi Han, Zejia Fan, Qi Dai +4
Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in L…