3 accepted papers
In this paper, we tackle the high computational overhead of Transformers for efficient image super-resolution (SR). Motivated by the observations of self-attention's inter-layer repetition, we introduce a convolutionized self-attention module named Convolutional Attention (ConvAttn) that emulates se…
Domain generalization (DG) aims to adapt a model using one or multiple source domains to ensure robust performance in unseen target domains. Recently, Parameter-Efficient Fine-Tuning (PEFT) of foundation models has shown promising results in the context of DG problem. Nevertheless, existing PEFT met…
Recently efficient Vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally they use 4x4 patch embeddings and a 4-stage structure at the macro level while utilizing sophisticated attention with multi-head configuration at the micro level. This…