activity
20172025
most citedCenterCLIP: Token Clustering for Efficient Text-Video Retrieval

128 citations · 284 across the 19 of their papers we have counts for

collaborators

32 papers

cs.CV2025

VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing

Xiangpeng Yang, Linchao Zhu, Hehe Fan +1

Recent advancements in diffusion models have significantly improved video generation and editing capabilities. However, multi-grained video editing, which encompasses class-level,…

cs.CV20251 cited

Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data

Chao Liang, Linchao Zhu, Zongxin Yang +2

We focus on the challenging problem of learning an unbiased classifier from a large number of potentially relevant but noisily labeled web images given only a few clean labeled ima…

cs.CV20232 cited

FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax

Yu Lu, Linchao Zhu, Hehe Fan +1

Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visu…

cs.CV202229 cited

Fine-Grained Semantically Aligned Vision-Language Pre-Training

Juncheng Li, Xin He, Longhui Wei +6

Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarit…

cs.CV2022128 cited

CenterCLIP: Token Clustering for Efficient Text-Video Retrieval

Shuai Zhao, Linchao Zhu, Xiaohan Wang +1

Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling com…

cs.CV20225 cited

Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning

Juncheng Li, Junlin Xie, Long Qian +6

Temporal grounding in videos aims to localize one target video segment that semantically corresponds to a given query sentence. Thanks to the semantic diversity of natural language…