10 papers
PRMU: A Corpus-Free Benchmark for Person-Centric Knowledge Unlearning in Multimodal Large Language Models
Huafeng Chen, Yueming Lyu, Ziyuan Chen +4
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in storing and recalling rich person-related knowledge, raising increasing concerns about reliabl…
Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection
Huafeng Chen, Yueming Lyu, Chenyang Si +3
Camouflaged object detection (COD) aims to segment objects that are visually concealed in their surroundings and has attracted increasing attention in recent years. However, most e…
AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling
Jiajun Liang, Yucheng Liao, Yukang Cao +12
Language remains an outlier in generative modeling: while images, video, and audio are increasingly modeled in continuous latent spaces, text generation still relies predominantly…
TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents
Zhiqiang Liu, Wenhui Dong, Yilang Tan +3
Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect inte…
NGM: A Plug-and-Play Training-Free Memory Module for LLMs
Yuwen Qu, Wenhui Dong, Chenyang Si +1
Recent studies introduce conditional memory modules that decouple knowledge storage from neural computation, enabling more direct knowledge access. Compared to MoE, which relies on…
One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Control
Haoxiang Rao, Zhao Wang, Chenyang Si +4
Industrial anomaly detection (AD) is characterized by an abundance of normal images but a scarcity of anomalous ones. Although numerous few-shot anomaly synthesis methods have been…