3 citations · 6 across the 21 of their papers we have counts for
14 papers · 1 filter
SoccerNet 2026 Challenges Results
Anthony Cioppa, Silvio Giancola, Håkan Ardö +102
The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…
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.…
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…
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…
GEditBench v2: A Human-Aligned Benchmark for General Image Editing
Zhangqi Jiang, Zheng Sun, Xianfang Zeng +7
Recent advances in image editing have enabled models to handle complex instructions with impressive realism. However, existing evaluation frameworks lag behind: current benchmarks…
OpusAnimation: Code-Based Dynamic Chart Generation
Bozheng Li, Miao Yang, Zhenhan Chen +9
Dynamic Chart Generation (DCG) involves producing code-rendered animated visualizations as charts. While recent advances in multi-modal large language models (MLLMs) have significa…
SoccerNet 2025 Challenges Results
Silvio Giancola, Anthony Cioppa, Marc Gutiérrez-Pérez +115
The SoccerNet 2025 Challenges mark the fifth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in football video understandi…
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought
Yi Lu, Jiawang Cao, Yongliang Wu +6
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable reasoning capability while lack explicit mechanisms for visual grounding and segmentation, creating a gap bet…
KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models
Yongliang Wu, Zonghui Li, Xinting Hu +7
Recent advances in multi-modal generative models have enabled significant progress in instruction-based image editing. However, while these models produce visually plausible output…
VEU-Bench: Towards Comprehensive Understanding of Video Editing
Bozheng Li, Yongliang Wu, Yi Lu +7
Widely shared videos on the internet are often edited. Recently, although Video Large Language Models (Vid-LLMs) have made great progress in general video understanding tasks, thei…
Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark
Yongliang Wu, Wenbo Zhu, Jiawang Cao +8
The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times. Despite notable advancements in the fields of v…
Number it: Temporal Grounding Videos like Flipping Manga
Yongliang Wu, Xinting Hu, Yuyang Sun +5
Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue. However, they struggle to extend this visual understanding…
Zero-Shot Long-Form Video Understanding through Screenplay
Yongliang Wu, Bozheng Li, Jiawang Cao +8
The Long-form Video Question-Answering task requires the comprehension and analysis of extended video content to respond accurately to questions by utilizing both temporal and cont…
Reframe Anything: LLM Agent for Open World Video Reframing
Jiawang Cao, Yongliang Wu, Weiheng Chi +3
The proliferation of mobile devices and social media has revolutionized content dissemination, with short-form video becoming increasingly prevalent. This shift has introduced the…