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 …

As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model
on multiple source domains and then directly generalize to an arbitrary unseen target …

Domain generalization aims to reduce the vulnerability of deep neural networks in the out-of-
domain distribution scenario. With the recent and increasing data privacy concerns …

Federated learning (FL) enables distributed clients to collaboratively learn a global model,
suggesting its potential for use in improving data privacy in machine learning. However …

We introduce an innovative multi-resolution framework for encoding and interactively
visualizing large relightable images using a neural reflectance model derived from a state-of …

Domain generalization (DG) aims to train a model with access to a limited number of source
domains for generalizing it across various unseen target domains. The key to solving the DG …

Most existing domain adaptation methods learn with both (labeled) samples in the source
domain and (unlabeled) samples in the target domain. Relying on the availability of target …

This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm
for optimization under domain shifts. It is motivated by the inconsistent convergence degree …

Domain generalization aims at generalizing the network trained on multiple domains to
unknown but related domains. Under the assumption that different domains share the same …