Grafting Vision Transformers

Jongwoo Park, Kumara Kahatapitiya, Donghyun Kim, Shivchander Sudalairaj, Quanfu Fan, Michael S Ryoo

Keywords: Vision Transformers, Multi-scale, Multi-branch, Grafting, Classification, Semantic segmentation, Object detection

TL;DR: We present a simple and efficient add-on component (termed GrafT) that considers global dependencies and multi-scale information. GrafT can be easily adopted in both homogeneous (ViT) and pyramid (Swin) Transformers.

Abstract: Vision Transformers (ViTs) have recently become the state-of-the-art across many computer vision tasks. In contrast to convolutional networks (CNNs), ViTs enable global information sharing even within shallow layers of a network, i.e., among high-resolution features. However, this perk was later overlooked with the success of pyramid architectures such as Swin Transformer, which show better performance-complexity trade-offs. In this paper, we present a simple and efficient add-on component (termed GrafT) that considers global dependencies and multi-scale information throughout the network, in both high- and low-resolution features alike. GrafT can be easily adopted in both homogeneous and pyramid Transformers while showing consistent gains. It has the flexibility of branching- out at arbitrary depths, widening a network with multiple scales. This grafting operation enables us to share most of the parameters and computations of the backbone, adding only minimal complexity, but with a higher yield. In fact, the process of progressively compounding multi-scale receptive fields in GrafT enables communications between local regions. We show the benefits of the proposed method on multiple benchmarks, including image classification (ImageNet-1K), semantic segmentation (ADE20K), object detection and instance segmentation (COCO2017). Our code and models will be made available.

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