1 citations · 2 across the 17 of their papers we have counts for
9 papers · 1 filter
Zoom-Zero: Reinforced Coarse-to-Fine Video Understanding via Temporal Zoom-in
Xiaoqian Shen, Min-Hung Chen, Yu-Chiang Frank Wang +2
Grounded video question answering (GVQA) aims to localize relevant temporal segments in videos and generate accurate answers to a given question; however, large video-language mode…
4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation
Chiao-An Yang, Ryo Hachiuma, Sifei Liu +4
Despite advances in Multimodal LLMs (MLLMs), their ability to reason over 3D structures and temporal dynamics remains limited, constrained by weak 4D perception and temporal unders…
Unified Reinforcement and Imitation Learning for Vision-Language Models
Byung-Kwan Lee, Ryo Hachiuma, Yong Man Ro +2
Vision-Language Models (VLMs) have achieved remarkable progress, yet their large scale often renders them impractical for resource-constrained environments. This paper introduces U…
Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation
Yusuke Hirota, Ryo Hachiuma, Boyi Li +9
Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-worl…
Autoregressive Universal Video Segmentation Model
Miran Heo, Sukjun Hwang, Min-Hung Chen +4
Recent video foundation models such as SAM2 excel at prompted video segmentation by treating masks as a general-purpose primitive. However, many real-world settings require unpromp…
LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Yusuke Hirota, Boyi Li, Ryo Hachiuma +7
Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating de…