1 citations · 1 across the 6 of their papers we have counts for
7 papers
Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection
Runhe Lai, Xinhua Lu, Kanghao Chen +3
In trustworthy medical diagnosis systems, integrating out-of-distribution (OOD) detection aims to identify unknown diseases in samples, thereby mitigating the risk of misdiagnosis.…
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Xinhua Lu, Runhe Lai, Yanqi Wu +3
Pre-trained vision-language models (VLMs) have advanced out-of-distribution (OOD) detection recently. However, existing CLIP-based methods often focus on learning OOD-related knowl…
FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector
Jiankang Chen, Ling Deng, Zhiyong Gan +2
Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model's over…
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…
Class Incremental Learning with Task-Specific Batch Normalization and Out-of-Distribution Detection
Zhiping Zhou, Xuchen Xie, Yiqiao Qiu +3
This study focuses on incremental learning for image classification, exploring how to reduce catastrophic forgetting of all learned knowledge when access to old data is restricted.…
Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment
Angchi Xu, Wei-Shi Zheng
Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (order…