The paper provides a comprehensive review of the research on adapting Vision Transformers (ViTs) to handle distribution shifts in computer vision tasks. It covers the fundamentals and architecture of ViTs, and then delves into the various strategies employed for Domain Adaptation (DA) and Domain Generalization (DG).
For DA, the paper categorizes the research into feature-level adaptation, instance-level adaptation, model-level adaptation, and hybrid approaches. Feature-level adaptation focuses on aligning feature distributions between source and target domains. Instance-level adaptation prioritizes specific data points that better reflect the target domain characteristics. Model-level adaptation involves developing specialized ViT architectures or layers to enhance adaptability. Hybrid approaches combine multiple adaptation techniques.
The paper also discusses diverse strategies used to enhance DA, such as adversarial learning, cross-domain knowledge transfer, visual prompts, self-supervised learning, hybrid networks, knowledge distillation, source-free adaptation, test-time adaptation, and pseudo-label refinement.
For DG, the paper explores multi-domain learning, meta-learning, regularization techniques, and data augmentation strategies to enable ViTs to generalize well to unseen domains.
The comprehensive tables provided in the paper offer valuable insights into the various approaches researchers have taken to address distribution shifts by integrating ViTs. The findings highlight the versatility of ViTs in managing distribution shifts, which is crucial for real-world applications, especially in critical safety and decision-making scenarios.
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by Shadi Alijan... at arxiv.org 04-09-2024
https://arxiv.org/pdf/2404.04452.pdfDeeper Inquiries