Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …

The prior monitoring of the road surface conditions provides valuable information to vehicle
trajectory planning and active control systems. Road surface perception with vision sensors …

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by
using a joint prediction from a conventional 3D model and a well-pretrained 2D model …

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One
reason is that existing robustness benchmarks are limited, as they either rely on synthetic …

This paper presents an investigation into long-tail video recognition. We demonstrate that,
unlike naturally-collected video datasets and existing long-tail image benchmarks, current …

Federated learning (FL), training deep models from decentralized data without privacy
leakage, has shown great potential in medical image computing recently. However …

Estimating heterogeneous treatment effects (HTE) is crucial for identifying the variation of
treatment effects across individuals or subgroups. Most existing methods estimate HTE by …

Synthetic aperture radar (SAR) ship detection is an important technology supporting water
traffic monitoring and marine safety maintenance. In recent years, many methods based on …

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets
but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples …