We present prompt distribution learning for effectively adapting a pre-trained vision-
language model to address downstream recognition tasks. Our method not only learns low …

Images captured with improper exposures usually bring unsatisfactory visual effects.
Previous works mainly focus on either underexposure or overexposure correction, resulting …

In this paper, we focus on multi-task classification, where related classification tasks share
the same label space and are learned simultaneously. In particular, we tackle a new setting …

Domain generalization (DG) aims at learning a model on source domains to well generalize
on the unseen target domain. Although it has achieved great success, most of the existing …

As machine learning models continue to achieve impressive performance across different
tasks, the importance of effective anomaly detection for such models has increased as well …

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

Human-object interaction (HOI) detectors with popular query-transformer architecture have
achieved promising performance. However, accurately identifying uncommon visual …

Domain generalization primarily mitigates domain shift among multiple source domains,
generalizing the trained model to an unseen target domain. However, the spurious …

Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated remarkable
effectiveness in learning generic visual representations. Several approaches aim to …