9 papers
MedXplore: Towards Reliable and Unbiased Generalized Category Discovery in Medical Imaging
Jianwei He, Kailin Lyu, Junhao Dong +6
The paper presents MedXplore, a unified framework for generalized category discovery in medical imaging that leverages frequency-domain adaptive attention and an adaptive cosine-an…
Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
Tian Zhang, Yujia Tong, Junhao Dong +3
The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearning in quantized models. However…
Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
Wenbo Pan, Jie Xu, Qiguang Chen +5
Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability, w…
Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition
Yuchao Hou, Zixuan Zhang, Jie Wang +9
As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target recognition facilitates intelligent perce…
C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection
Siheng Wang, Zhengdao Li, Yanshu Li +12
Object detection has advanced significantly in the closed-set setting, but real-world deployment remains limited by two challenges: poor generalization to unseen categories and ins…
CoopQ: Cooperative Game Inspired Layerwise Mixed Precision Quantization for LLMs
Junchen Zhao, Ali Derakhshan, Jayden Kana Hyman +3
Large Language Models (LLMs) promise impressive capabilities, yet their multi-billion-parameter scale makes on-device or low-resource deployment prohibitive. Mixed-precision quanti…