EPIC-Bench: 視覚言語モデルにおける細粒度な身体化視覚グラウンディングのための知覚中心ベンチマーク
EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
身体化エージェントの視覚基盤として使われる視覚言語モデル（VLM）の、実際の環境での細かい視覚認識能力を評価するベンチマークEPIC-Benchを提案。6.6kの注釈付きデータで、対象位置特定・ナビゲーション・操作の3段階にわたる23タスクを設定し、89以上のVLMを評価した。
著者: Haozhe Shan, Xiancong Ren, Han Dong, Haoyuan Shi, Yingji Zhang, Jiayu Hu, Yi Zhang, Yong Dai, Bin Shen, Lizhen Qu, Zenglin Xu, Xiaozhu Ju
分類: cs.CV
原文アブストラクト
While large vision-language models (VLMs) are increasingly adopted as the perceptual backbone for embodied agents, existing benchmarks often rely on question-answering or multiple-choice formats. These protocols allow models to exploit linguistic priors rather than demonstrating genuine visual grounding. To address this, we present EPIC-Bench, Embodied PerceptIon BenChmark, a fine-grained grounding benchmark designed to systematically evaluate the visual perceptual capabilities of VLMs in real-world embodied environments. Comprising 6.6k meticulously annotated tuples (Image, Text, Mask), EPIC-Bench spans 23 fine-grained tasks across three core stages of the embodied interaction pipeline: Target Localization, Navigation, and Manipulation. Extensive evaluations of over 89 leading VLMs reveal that while advanced reasoning models show promise, current VLMs universally struggle with complex visual-text alignment for physical interactions. Specifically, models exhibit critical bottlenecks in multi-target counting, part-whole relationship understanding, and affordance region detection. EPIC-Bench provides a robust foundation and actionable insights for advancing the next generation of vision-driven embodied models.