2 citations · 3 across the 5 of their papers we have counts for
5 papers
DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection
Yanqi Wu, Xinhua Lu, Runhe Lai +4
Recent studies show that using potential out-of-distribution (OOD) labels from large corpora as auxiliary information can improve OOD detection in vision-language models (VLMs). Ho…
DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectors
Yanqi Wu, Qichao Chen, Runhe Lai +5
Out-of-distribution (OOD) detection remains a fundamental challenge for deep neural networks, particularly due to overconfident predictions on unseen OOD samples during testing. We…
TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
Jiankang Chen, Tong Zhang, Wei-Shi Zheng +1
Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overco…
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases
Wentao Zhang, Yujun Huang, Tong Zhang +3
Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In…
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification
Chenghua Zeng, Huijuan Lu, Kanghao Chen +2
Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-th…