Abstract
This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive performance in image classification. To adopt ViT to object detection and dense prediction tasks, many works inherit the multistage design from convolutional networks and highly customized ViT architectures. Behind this design, the goal is to pursue a better trade-off between computational cost and effective aggregation of multiscale global contexts. However, existing works adopt the multistage architectural design as a black-box solution without a clear understanding of its true benefits. In this paper, we comprehensively study three architecture design choices on ViT – spatial reduction, doubled channels, and multiscale features – and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy. We further complete a scaling rule to optimize our model’s trade-off on accuracy and computation cost / model size. By leveraging a constant feature resolution and hidden size throughout the encoder blocks, we propose a simple and compact ViT architecture called Universal Vision Transformer (UViT) that achieves strong performance on COCO object detection and instance segmentation benchmark. Our code is available at https://github.com/tensorflow/models/tree/master/official/projects/uvit.
W. Chen—Work done during the first author’s research internship with Google.
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Notes
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For example, if the input sequence has \((896/8)\times (896/8) = 112\times 112\) tokens, a window of scale \(\frac{1}{16}\) will contain \(7\times 7 = 49\) elements. Similar ideas for \(\frac{1}{8}\) and \(\frac{1}{4}\).
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Chen, W. et al. (2022). A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_41
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