Recently, Large Language Models (LLMs) and Multimodal Large Language Models
(MLLMs) have shown promise in instruction following and 2D image understanding. While …

Since real-world machine systems are running in non-stationary environments, Continual
Test-Time Adaptation (CTTA) task is proposed to adapt the pre-trained model to continually …

We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation
which aims to adapt a source-trained model to the target domain without accessing the …

Since autonomous driving systems usually face dynamic and ever-changing environments,
continual test-time adaptation (CTTA) has been proposed as a strategy for transferring …

With the exponential growth in the speed and volume of remote sensing data, deep learning
models are expected to adapt and continually learn over time. Unfortunately, the domain …

Federated learning (FL) offers a privacy-centric distributed learning framework, enabling
model training on individual clients and central aggregation without necessitating data …

The visual prompts have provided an efficient manner in addressing visual cross-domain
problems. Previous works introduce domain prompts to tackle the classification Test-Time …

The Transformer-based detectors (ie, DETR) have demonstrated impressive performance on
end-to-end object detection. However, transferring DETR to different data distributions may …

For real-world applications, neural network models are commonly deployed in dynamic
environments, where the distribution of the target domain undergoes temporal changes …

Current object re-identification (ReID) system follows the centralized processing paradigm,
ie, all computations are conducted in the cloud server and edge devices are only used to …