FasterViT: Fast Vision Transformers with Hierarchical Attention

Published: 16 Jan 2024, Last Modified: 15 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Vision Transformers, Classification, Detection, Instance Segmentation, Semantic Segmentation
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TL;DR: A general computer vision backbone to effectively learn both short and long-range spatial information.
Abstract: We design a new family of hybrid CNN-ViT neural networks, named FasterViT, with a focus on high image throughput for computer vision (CV) applications. FasterViT combines the benefits of fast local representation learning in CNNs and global modeling properties in ViT. Our newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs. We benefit from efficient window-based self-attention. Each window has access to dedicated carrier tokens that participate in local and global representation learning. At a high level, global self-attentions enable the efficient cross-window communication at lower costs. FasterViT achieves a SOTA Pareto-front in terms of accuracy and image throughput. We have extensively validated its effectiveness on various CV tasks including classification, object detection and segmentation. We also show that HAT can be used as a plug-and-play module for existing networks and enhance them. We further demonstrate significantly faster and more accurate performance than competitive counterparts for images with high resolution. Code is available at
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Primary Area: representation learning for computer vision, audio, language, and other modalities
Submission Number: 467