Vision Transformer (ViT) has shown great power in learning from large-scale datasets. However, collecting sufficient data for expert knowledge is always difficult. To handle this problem, Cross-Domain Few-Shot Learning (CDFSL) has been proposed to transfer the source-domain knowledge learned from sufficient data to target domains where only scarce data is available.

We extensively study the design space of these embeddings and highlight important design considerations.

However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability.

We present a novel approach for test-time adaptation via online self-training, consisting of two components.

Seq2Seq AP - 44.8 (e) Large vocabulary object detection.