Concept erasure is essential for the safe deployment of text-to-image diffusion models, as they may reproduce harmful, copyrighted, or privacy-sensitive content learned from unconstrained large-scale data. Existing methods typically erase unwanted concepts while preserving general generation capability by redirecting t...
Qian-Long Xiang, Miao Zhang, Kun Wang et al.· 0 citations
GoldenViewVQA requires models to jointly answer driving-scene questions and identify the camera view containing the supporting visual evidence, making precise evidence localization as important as answer correctness. We present \textbf{CoVeR-VQA}, a training-free multi-stage verification and correction framework for gr...
Kun Wang, Yu-Peng Hu, Ru-Ping Cao et al.· 0 citations
MM-ContextFold is proposed, a training-free framework that loads raw images only when needed and maintains a persistent, text-only main context for high-level planning and spawns ephemeral branch contexts for image-dependent subtasks.
Yang Tian, Fan Liu, Jing-Yuan Zhang et al.· 0 citations
The Lightweight Action Inversion and Riemannian rectification network (LightAIR) is proposed, which introduces textual semantic priors as anchors via a lightweight action inversion operator to extract pure action features, thereby overcoming visual-inherent coupling.
Yulun Zhang, Zixu Li, Zhiwei Chen et al.· 0 citations
A cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios, and two official Codabench tracks.
Yu-Qian Fu, Tianwen Qian, Yanjun Li et al.· 0 citations
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