6 citations · 10 across the 10 of their papers we have counts for
11 papers
TubeRMC: Tube-conditioned Reconstruction with Mutual Constraints for Weakly-supervised Spatio-Temporal Video Grounding
Jinxuan Li, Yi Zhang, Jian-Fang Hu +3
Spatio-Temporal Video Grounding (STVG) aims to localize a spatio-temporal tube that corresponds to a given language query in an untrimmed video. This is a challenging task since it…
Image-to-Video Transfer Learning based on Image-Language Foundation Models: A Comprehensive Survey
Jinxuan Li, Chaolei Tan, Haoxuan Chen +4
Image-Language Foundation Models (ILFMs) have demonstrated remarkable success in vision-language understanding, providing transferable multimodal representations that generalize ac…
HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning
Jun Li, Jinpeng Wang, Chaolei Tan +6
Partially Relevant Video Retrieval (PRVR) addresses the critical challenge of matching untrimmed videos with text queries describing only partial content. Existing methods suffer f…
Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation
Tianming Liang, Haichao Jiang, Yuting Yang +4
Referring video object segmentation (RVOS) aims to identify, track and segment the objects in a video based on language descriptions, which has received great attention in recent y…
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations
Tianming Liang, Kun-Yu Lin, Chaolei Tan +3
Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This is challenging as it involves deep vision-language un…
SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity Edge Detection
Xing Liufu, Chaolei Tan, Xiaotong Lin +3
Edge labels are typically at various granularity levels owing to the varying preferences of annotators, thus handling the subjectivity of per-pixel labels has been a focal point fo…
SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses
Chaolei Tan, Zihang Lin, Junfu Pu +7
Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video gro…
Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels
Tianming Liang, Chaolei Tan, Beihao Xia +2
This paper focuses on open-ended video question answering, which aims to find the correct answers from a large answer set in response to a video-related question. This is essential…
Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding
Chaolei Tan, Jianhuang Lai, Wei-Shi Zheng +1
Video Paragraph Grounding (VPG) is an emerging task in video-language understanding, which aims at localizing multiple sentences with semantic relations and temporal order from an…
STVGFormer: Spatio-Temporal Video Grounding with Static-Dynamic Cross-Modal Understanding
Zihang Lin, Chaolei Tan, Jian-Fang Hu +3
In this technical report, we introduce our solution to human-centric spatio-temporal video grounding task. We propose a concise and effective framework named STVGFormer, which mode…
Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding
Chaolei Tan, Zihang Lin, Jian-Fang Hu +2
We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In the first stage, we propose an A…