activity
20242026
most citedLearning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2026

STORM: End-to-End Referring Multi-Object Tracking in Videos

Zijia Lu, Jingru Yi, Jue Wang +4

Referring multi-object tracking (RMOT) is a task of associating all the objects in a video that semantically match with given textual queries or referring expressions. Existing RMO…

cs.CV2025

LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation

Yang Zhou, Shiyu Zhao, Yuxiao Chen +3

Large foundation models trained on large-scale vision-language data can boost Open-Vocabulary Object Detection (OVD) via synthetic training data, yet the hand-crafted pipelines oft…

cs.CV2025

The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models via Visual Information Steering

Zhuowei Li, Haizhou Shi, Yunhe Gao +7

Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded content…

cs.CV2024

Exploiting VLM Localizability and Semantics for Open Vocabulary Action Detection

Wentao Bao, Kai Li, Yuxiao Chen +3

Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detec…

cs.CV20241 cited

Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment

Yuxiao Chen, Kai Li, Wentao Bao +4

Learning to localize temporal boundaries of procedure steps in instructional videos is challenging due to the limited availability of annotated large-scale training videos. Recent…