Concept erasure removes copyright-protected, privacy-sensitive, or otherwise undesirable concepts from pretrained text-to-image diffusion models to support content governance and compliance. As erasure requests arrive over time, models must remove new targets without undoing prior erasures. Existing methods do not cons...
Yong-Liang Wu, Haori Lu, Jin-Qi Luo et al.· 0 citations
Concept erasure aims to remove a target concept, such as a copyrighted style, a recognizable character, or unsafe content, from a pretrained text-to-image diffusion model while preserving its ability to generate other content. Existing activation steering methods build an erasure direction mainly from the target concep...
Yong-Liang Wu, Haori Lu, Yu-Lun Wu et al.· 0 citations
Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order and is therefore poorly suited to the bi...
Wan-Jiang Weng, Yong-Liang Wu, Xiaofeng Tan et al.· 0 citations
Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
A. Cioppa, Silvio Giancola, Haakan Ardo et al.· 1 citation
A comprehensive external and internal investigation of multimodal in-context learning on the image captioning task is conducted, revealing both how ICEs configuration strategies impact model performance through external experiments and characteristic typical patterns through internal inspection.
Li Li, Yongliang Wu, Jingze Zhu et al.· IEEE Transactions on Pattern...· 0 citations
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