5 citations · 18 across the 18 of their papers we have counts for
5 papers · 1 filter
CODI: Improving Self-Supervised Vision Transformers with Cross Distillation
Siran Dai, Qianqian Xu, Peisong Wen +3
Self-supervised Vision Transformers learn strong image-level representations, but their patch-level representations can be unreliable for dense prediction. We study this mismatch a…
When 1+1 Backfires: Rethinking Relevance-Diversity Balancing for Sample-Aware Visual Token Reduction
Yihang Huang, Peisong Wen, Chenhao Zhou +2
Visual token compression has emerged as a critical technique for reducing the inference cost of Vision-Language Models. Existing methods select tokens based on text relevance, visu…
Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning
Yang Liu, Qianqian Xu, Peisong Wen +2
Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may specify the desired result t…
The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?
Xingyu Lyu, Qianqian Xu, Zhiyong Yang +2
Gated Linear Units (GLU) and their variants are widely adopted in modern open-source large language model architectures and consistently outperform their non-gated counterparts, ye…
From Static to Dynamic: Exploring Self-supervised Image-to-Video Representation Transfer Learning
Yang Liu, Qianqian Xu, Peisong Wen +3
Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video…