← 研踪 PaperTrailarXiv:2509.02807 · v2
PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?
首次提交 2025-09-03 04:21
版本更新 2026-10-26 14:56
雷达观测 2026-10-29 00:36
摘要
Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension to video has enabled tasks such as video question answering and video captioning, their pixel-level visual grounding abilities are less studied. In this work, we raise the pertinent question of whether motion is used in pixel-level visual grounding and whether video MLLMs can segment objects based on natural language expressions describing their motion patterns. We identify the shortcomings in the current benchmarks, where we show that a single frame can often suffice for capturing the referring expression without any temporal reasoning. To address this, we introduce novel motion-centric probing, particularly designed for the visual grounding task, to study video MLLMs' ability to identify true motion from a fake one and their ability to grasp the ordering of motion. Consequently, we introduce MoCentric-Bench, a motion-centric benchmark designed to evaluate video MLLMs based on their ability to capture the interaction between motion and language, rather than relying primarily on static cues. We further establish strong single-image baselines that either match or surpass prior methods. Finally, we explore a simple motion-centric adaptation that provides state-of-the-art performance on our MoCentric-Bench. Code is available at https://msiam.github.io/PixFoundationSeries/.
作者备注：Accepted in NeurIPS 2026, Evaluations and Datasets Track
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v2 · 2026-10-26 14:56 · 最新观测版本
PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?
Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension to video has enabled tasks such as video question answering and video captioning, their pixel-level visual grounding abilities are less studied. In this work, we raise the pertinent question of whether motion is used in pixel-level visual grounding and whether video MLLMs can segment objects based on natural language expressions describing their motion patterns. We identify the shortcomings in the current benchmarks, where we show that a single frame can often suffice for capturing the referring expression without any temporal reasoning. To address this, we introduce novel motion-centric probing, particularly designed for the visual grounding task, to study video MLLMs' ability to identify true motion from a fake one and their ability to grasp the ordering of motion. Consequently, we introduce MoCentric-Bench, a motion-centric benchmark designed to evaluate video MLLMs based on their ability to capture the interaction between motion and language, rather than relying primarily on static cues. We further establish strong single-image baselines that either match or surpass prior methods. Finally, we explore a simple motion-centric adaptation that provides state-of-the-art performance on our MoCentric-Bench. Code is available at https://msiam.github.io/PixFoundationSeries/.
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