Dan Zlotnikov, Ohad Ben-Shahar · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28419 · open-access PDF
视觉语言跟踪器(VLTs)接受旨在协助跟踪的描述,但接受文本并不能确定其内容会改变跟踪决策,特别是视觉证据恶化时。我们引入了DISTACT,一个匹配干预基准,该基准保持视频,目标和分散器固定,同时跨过四个文本前列,沿着一个受控的严重性轴的四个图像降解。
Machine translation (Meta M2M100); the English original follows.
Vision-language trackers (VLTs) accept descriptions intended to assist tracking, but accepting text does not establish that its content changes tracking decisions, particularly as visual evidence deteriorates. We introduce DISTRACT, a matched-intervention benchmark that holds video, target, and distractor fixed while crossing four text priors with four image degradations along a controlled severity axis. We evaluate five VLTs on four datasets using behavioral contrasts, layerwise representation probes, and activation patching. Molmo2 and JointNLT exhibit directed text use. Some effects contrac
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