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20242026
most citedEAGLE: Egocentric AGgregated Language-video Engine

3 citations · 7 across the 14 of their papers we have counts for

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13 papers · 1 filter

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.CV20261 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.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.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…