2023/11/13 by L. P. Erickson, Erickson, Lily · 1 citation
Computer Science · Engineering · #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #CCD and CMOS Imaging Sensors #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques
paper · pdf · doi:10.48550/arxiv.2311.07184
openalex publication_date 2023/11/13 · openalex created_date 2023/11/15 · openalex updated_date 2026/07/28
Despite lagging behind their modal cousins in many respects, Vision Transformers have provided an interesting opportunity to bridge the gap between sequence modeling and image modeling. Up until now however, vision transformers have largely been held back, due to both computational inefficiency, and lack of proper handling of spatial dimensions. In this paper, we introduce the Cross-Axis Transformer. CAT is a model inspired by both Axial Transformers, and Microsoft's recent Retentive Network, that drastically reduces the required number of floating point operations required to process an image, while simultaneously converging faster and more accurately than the Vision Transformers it replaces.