Active learning (AL) aims to improve model performance within a fixed labeling budget by
choosing the most informative data points to label. Existing AL focuses on the single-domain …

Domain Generalization (DG) aims to improve the generalization ability of models trained on
a specific group of source domains, enabling them to perform well on new, unseen target …

Data augmentation for minority classes is an effective strategy for long-tailed recognition,
thus developing a large number of methods. Although these methods all ensure the balance …

Object detection in urban scenarios is crucial for autonomous driving in intelligent traffic
systems. However, unlike conventional object detection tasks, urban-scene images vary …

The vision-language pre-training has enabled deep models to make a huge step forward in
generalizing across unseen domains. The recent learning method based on the vision …

Federated learning (FL) is an evolving machine learning technique that allows collaborative
model training without sharing the original data among participants. In real-world scenarios …

Accurate morphological classification of white blood cells (WBCs) is an important step in the
diagnosis of leukemia, a disease in which nonfunctional blast cells accumulate in the bone …

Diabetic Retinopathy (DR), induced by diabetes, poses a significant risk of visual
impairment. Accurate and effective grading of DR aids in the treatment of this condition. Yet …

Deploying deep models in real-world scenarios entails a number of challenges, including
computational efficiency and real-world (eg, long-tailed) data distributions. We address the …

Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach
sight-threatening levels. Accurate and robust detection of DR severity is critical for the timely …