activity
20242026
most citedEAGLE: Egocentric AGgregated Language-video Engine

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

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8 papers · 1 filter

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…