10 papers · 1 filter
Disentangling Tabular Data Towards Better One-Class Anomaly Detection
Jianan Ye, Zhaorui Tan, Yijie Hu +3
Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a si…
Covariance-based Space Regularization for Few-shot Class Incremental Learning
Yijie Hu, Guanyu Yang, Zhaorui Tan +3
Few-shot Class Incremental Learning (FSCIL) presents a challenging yet realistic scenario, which requires the model to continually learn new classes with limited labeled data (i.e.…
Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation
Zhaorui Tan, Xi Yang, Tan Pan +8
Variations in medical imaging modalities and individual anatomical differences pose challenges to cross-modality generalization in multi-modal tasks. Existing methods often concent…
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification
Zhaorui Tan, Xi Yang, Qiufeng Wang +2
Vision models excel in image classification but struggle to generalize to unseen data, such as classifying images from unseen domains or discovering novel categories. In this paper…
CathAction: A Benchmark for Endovascular Intervention Understanding
Baoru Huang, Tuan Vo, Chayun Kongtongvattana +35
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are of…
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment
Tianyi Liu, Zhaorui Tan, Muyin Chen +3
Brain tumor segmentation is often based on multiple magnetic resonance imaging (MRI). However, in clinical practice, certain modalities of MRI may be missing, which presents a more…
Revisiting Mutual Information Maximization for Generalized Category Discovery
Zhaorui Tan, Chengrui Zhang, Xi Yang +2
Generalized category discovery presents a challenge in a realistic scenario, which requires the model's generalization ability to recognize unlabeled samples from known and unknown…
SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Kai Yao, Zhaorui Tan, Zixian Su +3
Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open…
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
Tianyi Liu, Zhaorui Tan, Kaizhu Huang +1
Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information. Despite recent e…
Rethinking Multi-domain Generalization with A General Learning Objective
Zhaorui Tan, Xi Yang, Kaizhu Huang
Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping. How…