Neural network based computer vision systems are typically built on a backbone, a
pretrained or randomly initialized feature extractor. Several years ago, the default option was …

In a joint vision-language space, a text feature (eg, from" a photo of a dog") could effectively
represent its relevant image features (eg, from dog photos). Also, a recent study has …

Large pre-trained transformers are $\textit {show-stealer} $ in modern-day deep learning,
and it becomes crucial to comprehend the parsimonious patterns that exist within them as …

In recent years, prompt tuning has proven effective in adapting pre-trained vision-language
models to down-stream tasks. These methods aim to adapt the pre-trained models by …

Contrastive learning is a highly effective method for learning representations from unlabeled
data. Recent works show that contrastive representations can transfer across domains …

The development of vision models for real-world applications is hindered by the challenge of
annotated data scarcity, which has necessitated the adoption of data-efficient visual learning …

In domain generalization (DG), most existing methods aspire to fine-tune a specific
pretrained model through novel DG algorithms. In this paper, we propose an alternative …

Deep Learning models have shown remarkable performance in a broad range of vision
tasks. However, they are often vulnerable against domain shifts at test-time. Test-time …

In recent years, several efforts have been aimed at improving the robustness of vision
models to domains and environments unseen during training. An important practical …

Large Language Models (LLMs) have become instrumental in advancing software
engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. AI …