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Under a strict cross-dataset zero-shot protocol on five IAD benchmarks, MOPDA outperforms the evaluated LVLM-based baselines on most detection, localization, and judgment metrics while remaining competitive with CLIP-based methods.
Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning. However, current LVLM-based IAD methods still struggle to produce precise pixel-level anomaly maps from generated language judgm...
This work proposes HOIMask, the first generative masked framework for modeling HOI motion in discrete space, and introduces a novel contact-aware reconstruction guidance in discrete space during inference, which fuses contact signals to optimize HOI tokens that forces the generated motion with higher spatio-temporal co...
HUG-VIS, a unified benchmark for Human-centered Understanding and Generation in Visual Intelligence, contains 8,400 seated half-body videos of 30 professional actors, each performing the same 280 emotion-action-prompt assignments under a controlled Mandarin studio protocol, with synchronized video, audio, text, and alp...
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