14 citations · 44 across the 10 of their papers we have counts for
10 papers
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection
Xincheng Yao, Ruoqi Li, Zefeng Qian +2
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD methods didn'…
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images
Min Shi, Anagha Lokhande, Mojtaba S. Fazli +10
Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e.g., glaucoma). For comp…
Learning to Predict Gradients for Semi-Supervised Continual Learning
Yan Luo, Yongkang Wong, Mohan Kankanhalli +1
A key challenge for machine intelligence is to learn new visual concepts without forgetting the previously acquired knowledge. Continual learning is aimed towards addressing this c…
Learning to Minimize the Remainder in Supervised Learning
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
The learning process of deep learning methods usually updates the model's parameters in multiple iterations. Each iteration can be viewed as the first-order approximation of Taylor…
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
Ye Liu, Huifang Li, Chao Hu +3
The task of instance segmentation in remote sensing images, aiming at performing per-pixel labeling of objects at instance level, is of great importance for various civil applicati…
Learning to Predict Trustworthiness with Steep Slope Loss
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
Understanding the trustworthiness of a prediction yielded by a classifier is critical for the safe and effective use of AI models. Prior efforts have been proven to be reliable on…