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

1 citations · 1 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2026

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

Rui Yang, Qianhui Wu, Yuxi Chen +7

Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despite rapid progress, the stronges…

cs.CV2026

Temporal Evidence Routing with Structured Visual Evidence for TimeLogicQA

Yuyang Sun, Yongliang Wu, Xingyu Zhu +8

TimeLogicQA evaluates whether video question answering systems can reason over temporal relations such as event existence, ordering, persistence, boundary conditions, and overlap.…

cs.CV2026

Adaptive Dense Evidence Refinement for Video Relational Reasoning for VRR-QA Challenge

Yuyang Sun, Yongliang Wu, Xingyu Zhu +8

VRR-QA evaluates whether video-language systems can infer spatial, temporal, viewpoint, depth, and visibility relations that are not always resolved by a single frame. We present a…

cs.CV2026

Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R

Yuyang Sun, Yongliang Wu, Xingyu Zhu +8

We describe \emph{Dual-Route Top-K Retrieval with 1v1 VLM Reranking} for the CoVR-R challenge. The method treats composed video retrieval as two coupled problems: finding a suffici…

cs.CY2026

Measuring and Mitigating Bias in Code Generated by Large Language Models

Yuxi Chen, Yutian Tang, Timothy Storer

Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation tasks. However, concerns abou…

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.CV2024★ 1 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…