Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

Transformers have recently gained significant attention in the computer vision community.
However, the lack of scalability of self-attention mechanisms with respect to image size has …

Vision transformer (ViT) recently has drawn great attention in computer vision due to its
remarkable model capability. However, most prevailing ViT models suffer from huge number …

The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution
before processing them with computer vision models has not yet been successfully …

While large pre-trained models have enabled impressive results on a variety of downstream
tasks, the largest existing models still make errors, and even accurate predictions may …

Vision Transformers convert images to sequences by slicing them into patches. The size of
these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

Transformers have demonstrated great power in the recent development of large
foundational models. In particular, the Vision Transformer (ViT) has brought revolutionary …

Vision transformers have recently achieved competitive results across various vision tasks
but still suffer from heavy computation costs when processing a large number of tokens …