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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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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References
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., Schmid, C.: Vivit: a video vision transformer. Ar**v abs/2103.15691 (2021)
Beal, J., Kim, E., Tzeng, E., Park, D.H., Zhai, A., Kislyuk, D.: Toward transformer-based object detection. ar**v preprint ar**v:2012.09958 (2020)
Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. ar**v preprint ar**v:2004.05150 (2020)
Cai, Z., Vasconcelos, N.: Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Global context networks. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. ar**v preprint ar**v:1706.05587 (2017)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Chen, X., Hsieh, C.J., Gong, B.: When vision transformers outperform resnets without pretraining or strong data augmentations. ar**v preprint ar**v:2106.01548 (2021)
Chen, Y., Zhang, Z., Cao, Y., Wang, L., Lin, S., Hu, H.: Reppoints v2: verification meets regression for object detection. Adv. Neural Inf. Process. Syst. 33, 5621–5631 (2020)
Chu, X., Zhang, B., Tian, Z., Wei, X., **a, H.: Do we really need explicit position encodings for vision transformers? ar**v e-prints, pp. ar**v-2102 (2021)
Cohen, N., Sharir, O., Shashua, A.: On the expressive power of deep learning: a tensor analysis. In: Conference on Learning Theory, pp. 698–728. PMLR (2016)
Crotts, A.P.S.: Vatt/columbia microlensing survey of m31 and the galaxy. ar**v: Astrophysics (1996)
Dai, J., Qi, H., **ong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. ar**v preprint ar**v:2010.11929 (2020)
Elbrächter, D., Perekrestenko, D., Grohs, P., Bölcskei, H.: Deep neural network approximation theory. ar**v preprint ar**v:1901.02220 (2019)
Eldan, R., Shamir, O.: The power of depth for feedforward neural networks. In: Conference on Learning Theory, pp. 907–940. PMLR (2016)
Han, K., **ao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. ar**v preprint ar**v:2103.00112 (2021)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Heo, B., Yun, S., Han, D., Chun, S., Choe, J., Oh, S.J.: Rethinking spatial dimensions of vision transformers. ar**v preprint ar**v:2103.16302 (2021)
Hu, H., Zhang, Z., **e, Z., Lin, S.: Local relation networks for image recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3464–3473 (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Huang, Z., Ben, Y., Luo, G., Cheng, P., Yu, G., Fu, B.: Shuffle transformer: rethinking spatial shuffle for vision transformer. ar**v preprint ar**v:2106.03650 (2021)
Liang, S., Srikant, R.: Why deep neural networks for function approximation? ar**v preprint ar**v:1610.04161 (2016)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. ar**v preprint ar**v:2103.14030 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. ar**v preprint ar**v:1711.05101 (2017)
Naseer, M., Ranasinghe, K., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Intriguing properties of vision transformers. ar**v preprint ar**v:2105.10497 (2021)
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? ar**v preprint ar**v:2108.08810 (2021)
Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., Shlens, J.: Stand-alone self-attention in vision models. ar**v preprint ar**v:1906.05909 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ar**v preprint ar**v:1409.1556 (2014)
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843–852 (2017)
Sun, P., et al.: Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454–14463 (2021)
Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks. ar**v preprint ar**v:1905.11946 (2019)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. ar**v preprint ar**v:2012.12877 (2020)
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., Jégou, H.: Going deeper with image transformers. ar**v preprint ar**v:2103.17239 (2021)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017)
Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. ar**v preprint ar**v:2102.12122 (2021)
**e, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. ar**v preprint ar**v:2105.15203 (2021)
Yuan, L., et al.: Tokens-to-token vit: training vision transformers from scratch on imagenet. ar**v preprint ar**v:2101.11986 (2021)
Zhai, X., Kolesnikov, A., Houlsby, N., Beyer, L.: Scaling vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12104–12113 (2022)
Zhao, H., Jia, J., Koltun, V.: Exploring self-attention for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10076–10085 (2020)
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)
Zhou, D., et al.: Deepvit: towards deeper vision transformer. ar**v preprint ar**v:2103.11886 (2021)
Zoph, B., et al.: Rethinking pre-training and self-training. ar**v preprint ar**v:2006.06882 (2020)
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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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