1 citations · 2 across the 10 of their papers we have counts for
10 papers
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
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…
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…
OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learning
Mushui Liu, Bozheng Li, Yunlong Yu
Recent Vision-Language Models (VLMs) \textit{e.g.} CLIP have made great progress in video recognition. Despite the improvement brought by the strong visual backbone in extracting s…
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners
Mushui Liu, Bozheng Li, Yunlong Yu
Prompt tuning, which involves training a small set of parameters, effectively enhances the pre-trained Vision-Language Models (VLMs) to downstream tasks. However, they often come a…
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…
Bang-Bang Control Of A Tail-less Morphing Wing Flight
Eric Sihite, Xintao Hu, Bozhen Li +3
Bats' dynamic morphing wings are known to be extremely high-dimensional, and they employ the combination of inertial dynamics and aerodynamics manipulations to showcase extremely a…