15 citations · 29 across the 25 of their papers we have counts for
7 papers · 1 filter
Towards Unsupervised Model Selection for Domain Adaptive Object Detection
Hengfu Yu, Jinhong Deng, Wen Li +1
Evaluating the performance of deep models in new scenarios has drawn increasing attention in recent years. However, while it is possible to collect data from new scenarios, the ann…
ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
Yuhang Yang, Jinhong Deng, Wen Li +1
While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle…
Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
Fanyue Wei, Wei Zeng, Zhenyang Li +3
Personalized text-to-image models allow users to generate varied styles of images (specified with a sentence) for an object (specified with a set of reference images). While remark…
Learning Semantic Latent Directions for Accurate and Controllable Human Motion Prediction
Guowei Xu, Jiale Tao, Wen Li +1
In the realm of stochastic human motion prediction (SHMP), researchers have often turned to generative models like GANS, VAEs and diffusion models. However, most previous approache…
Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition
Xunsong Li, Pengzhan Sun, Yangcen Liu +2
The interactions between human and objects are important for recognizing object-centric actions. Existing methods usually adopt a two-stage pipeline, where object proposals are fir…
Tuning-Free Adaptive Style Incorporation for Structure-Consistent Text-Driven Style Transfer
Yanqi Ge, Jiaqi Liu, Qingnan Fan +6
In this work, we target the task of text-driven style transfer in the context of text-to-image (T2I) diffusion models. The main challenge is consistent structure preservation while…
SSR: SAM is a Strong Regularizer for domain adaptive semantic segmentation
Yanqi Ge, Ye Huang, Wen Li +1
We introduced SSR, which utilizes SAM (segment-anything) as a strong regularizer during training, to greatly enhance the robustness of the image encoder for handling various domain…