517 citations · 1.7k across the 49 of their papers we have counts for
50 papers
Temporal Perceiving Video-Language Pre-training
Fan Ma, Xiaojie Jin, Heng Wang +4
Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve g…
Slimmable Networks for Contrastive Self-supervised Learning
Shuai Zhao, Linchao Zhu, Xiaohan Wang +1
Self-supervised learning makes significant progress in pre-training large models, but struggles with small models. Mainstream solutions to this problem rely mainly on knowledge dis…
Contrastive Video-Language Segmentation
Chen Liang, Yawei Luo, Yu Wu +1
We focus on the problem of segmenting a certain object referred by a natural language sentence in video content, at the core of formulating a pinpoint vision-language relation. Whi…
Saying the Unseen: Video Descriptions via Dialog Agents
Ye Zhu, Yu Wu, Yi Yang +1
Current vision and language tasks usually take complete visual data (e.g., raw images or videos) as input, however, practical scenarios may often consist the situations where part…
Less is More: Sparse Sampling for Dense Reaction Predictions
Kezhou Lin, Xiaohan Wang, Zhedong Zheng +2
Obtaining viewer responses from videos can be useful for creators and streaming platforms to analyze the video performance and improve the future user experience. In this report, w…
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation
Chen Liang, Yu Wu, Tianfei Zhou +4
Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference. Previous methods typically tackle RVOS through directly gr…