Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

With the emergence of pre-trained vision-language models like CLIP how to adapt them to
various downstream classification tasks has garnered significant attention in recent …

Despite substantial progress all-in-one image restoration (IR) grapples with persistent
challenges in handling intricate real-world degradations. This paper introduces MPerceiver …

The development of large vision-language models notably CLIP has catalyzed research into
effective adaptation techniques with a particular focus on soft prompt tuning. Conjointly test …

Recent progress in the few-shot adaptation of Vision-Language Models (VLMs) has further
pushed their generalization capabilities at the expense of just a few labeled samples within …

Test-time adaptation with pre-trained vision-language models has attracted increasing
attention for tackling distribution shifts during the test time. Though prior studies have …

Instance segmentation is data-hungry and as model capacity increases data scale becomes
crucial for improving the accuracy. Most instance segmentation datasets today require costly …

Pre-trained vision-language models (VLMs) have shown impressive results in various visual
classification tasks. However, we often fail to fully unleash their potential when adapting …

Self-supervised learning has achieved remarkable success in acquiring high-quality
representations from unlabeled data. The widely adopted contrastive learning framework …