activity
20242026
most citedEAGLE: Egocentric AGgregated Language-video Engine

3 citations · 8 across the 16 of their papers we have counts for

collaborators

18 papers

cs.CV2026

Unmentioned Checklist Findings Change How Reinforcement Learning Appears to Improve Chest Radiograph Report Checking

Ali Vosoughi, Akhil Kasturi, Chenliang Xu +1

Automated checks of radiology reports may rely on AI-generated checklists that leave findings unmentioned. We used reinforcement learning to train a vision-language model to fill i…

cs.CV2026

AdaTurn: Budget-Aware Test-Time Scaling for Active Visual Perception Agents

Susan Liang, Chao Huang, Filippos Bellos +3

Active visual agents solve fine-grained image tasks by interleaving reasoning with image-grounding actions across multiple turns. However, deployment-time rollout budgets are rarel…

cs.CV2026★ 1 cited

SurgAtlas: A Large-Scale Surgical Video-Language Dataset with 2,391 Hours of Open and Minimally Invasive Surgery

Filippos Bellos, Andre S. Gala-Garza, Miaowei Wang +8

We introduce SurgAtlas, the largest surgical video-language dataset to date, comprising 15,291 videos (2,391 hours) spanning 18 surgical specialties and over 5,000 procedure types,…

cs.CV2025

Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination

Yolo Y. Tang, Daiki Shimada, Hang Hua +4

Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fi…

cs.CV2025

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…

cs.LG2025

What to Do Next? Memorizing skills from Egocentric Instructional Video

Jing Bi, Chenliang Xu

Learning to perform activities through demonstration requires extracting meaningful information about the environment from observations. In this research, we investigate the challe…

cs.RO2025

ACTLLM: Action Consistency Tuned Large Language Model

Jing Bi, Lianggong Bruce Wen, Zhang Liu +1

This paper introduces ACTLLM (Action Consistency Tuned Large Language Model), a novel approach for robot manipulation in dynamic environments. Traditional vision-based systems ofte…

cs.MM2025

Can Sound Replace Vision in LLaVA With Token Substitution?

Ali Vosoughi, Jing Bi, Pinxin Liu +2

What happens when we push audio-visual alignment to its absolute limits? To systematically investigate this question, we needed datasets with granular alignment quality annotations…

cs.CV2025

Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting

Yunlong Tang, Jing Bi, Chao Huang +16

We present CAT-V (Caption AnyThing in Video), a training-free framework for fine-grained object-centric video captioning that enables detailed descriptions of user-selected objects…

cs.CL2025

Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)

Jing Bi, Susan Liang, Xiaofei Zhou +16

Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their re…

cs.CV2025

VERIFY: A Benchmark of Visual Explanation and Reasoning for Investigating Multimodal Reasoning Fidelity

Jing Bi, Junjia Guo, Susan Liang +8

Visual reasoning is central to human cognition, enabling individuals to interpret and abstractly understand their environment. Although recent Multimodal Large Language Models (MLL…

cs.CV2024★ 1 cited

Unveiling Visual Perception in Language Models: An Attention Head Analysis Approach

Jing Bi, Junjia Guo, Yunlong Tang +3

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable progress in visual understanding. This impressive leap raises a compelling question: ho…

cs.CV2024★ 1 cited

MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models

Hang Hua, Yunlong Tang, Ziyun Zeng +5

The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal understanding, enabling more sophisticated and accurate integration of visual and textual in…

cs.CV2024★ 3 cited

EAGLE: Egocentric AGgregated Language-video Engine

Jing Bi, Yunlong Tang, Luchuan Song +3

The rapid evolution of egocentric video analysis brings new insights into understanding human activities and intentions from a first-person perspective. Despite this progress, the…

cs.CV2024

CaRDiff: Video Salient Object Ranking Chain of Thought Reasoning for Saliency Prediction with Diffusion

Yolo Yunlong Tang, Gen Zhan, Li Yang +2

Video saliency prediction aims to identify the regions in a video that attract human attention and gaze, driven by bottom-up features from the video and top-down processes like mem…

cs.CV2024★ 1 cited

Do More Details Always Introduce More Hallucinations in LVLM-based Image Captioning?

Mingqian Feng, Yunlong Tang, Zeliang Zhang +1

Large Vision-Language Models (LVLMs) excel in integrating visual and linguistic contexts to produce detailed content, facilitating applications such as image captioning. However, u…

cs.CV2024

V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning

Hang Hua, Yolo Yunlong Tang, Chenliang Xu +1

Video summarization aims to create short, accurate, and cohesive summaries of longer videos. Despite the existence of various video summarization datasets, a notable limitation is…

cs.CV2024★ 1 cited

Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding

Yolo Yunlong Tang, Daiki Shimada, Jing Bi +3

Large language models (LLMs) have demonstrated remarkable capabilities in natural language and multimodal domains. By fine-tuning multimodal LLMs with temporal annotations from wel…