Camouflaged Object Detection (COD) aims to segment objects that are hidden within complex backgrounds. Due to the low visual contrast of camouflaged objects, annotations are costly, motivating unsupervised COD (UCOD) to eliminate labeling expenses. Most UCOD methods follow the “MLLMs + other foundation models + SAM” pa...
Hua-Feng Chen, Yueming Lyu, Caifeng Shan· Proceedings of the Thirty-Fi...· 0 citations
Findings show that task utility does not imply privacy or authorization compliance and that authorization in community settings remains an unresolved security challenge for persistent LLM agents.
Hao Chen, Wen-Hui Dong, Ye Chen et al.· 0 citations
This work proposes MME-Safety, a rigorously verified benchmark featuring a unique four-dimensional annotation schema that categorizes risk scenarios, harm severity, and modality-specific stealth levels and introduces a hierarchical evaluation framework to assess fundamental response reliability, actual risk exposure, a...
Yi Shi, Yueming Lyu, Hao-Xiang Tan et al.· 0 citations
This work introduces PRMU, a benchmark for evaluating corpus-free multimodal unlearning under realistic person-centric deletion requests, and introduces Similarity-Gated Projection Editing (SGPE), a lightweight corpus-free unlearning baseline with knowledge displacement, protected parameter-space editing, and locality-...
Hua-Feng Chen, Yueming Lyu, Ziyuan Chen et al.· 0 citations
AURORA-LM is introduced, a continuous-latent diffusion language model that separates the construction of a decodable text representation from the modeling of its distribution, and achieves the strongest performance among evaluated continuous and diffusion-based language models on OpenWebText free generation and XSum su...
Jiajun Liang, Yu-Ling Liao, Yu-Kang Cao et al.· 2 citations
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