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Vision transformer (ViT) recently has drawn great attention in computer vision due to its remarkable model capability.
Hoos, H.H., Stützle, T.: Stochastic local search: Foundations and applications. Elsevier (2004)
2004
Earlier work this paper cites.
Nilsback, M.E., Zisserman, A.: A visual vocabulary for flower classification. In: CVPR (2006)
2006
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Parkhi, O.M., Vedaldi, A., Zisserman, A., Jawahar, C.: Cats and dogs. In: CVPR (2012)
2012
Earlier work this paper cites.
3d object representations for fine-grained categorization. In: 3dRR (2013)
2013
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
Earlier work this paper cites.
O’Neill, M.E.: Pcg: A family of simple fast space-efficient statistically good algorithms for random number generation. TOMS (2014)
2014
Earlier work this paper cites.
Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv (2015)
2015
Earlier work this paper cites.
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv (2015)
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv (2016)
2016
Earlier work this paper cites.
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: ECCV (2016)
2016
Earlier work this paper cites.
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Frontiers in plant science (2016)
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: NeurIPS (2017)
2017
Earlier work this paper cites.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: CVPR (2017)
2017
Earlier work this paper cites.
Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: CVPR (2018)
2018
Earlier work this paper cites.
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: CVPR (2018)
2018
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
Earlier work this paper cites.
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: ICLR (2018)
2018
Earlier work this paper cites.
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: CVPR (2018)
2018
Earlier work this paper cites.
Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al.: Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv (2019)
2019
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1) (2019)
2019
Earlier work this paper cites.
Helber, P., Bischke, B., Dengel, A., Borth, D.: Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2019)
2019
Cited alongside, same era.
Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al.: Searching for mobilenetv3. In: ICCV (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. NeurIPS (2019)
2019
Cited alongside, same era.
Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: ICML (2019)
2019
Cited alongside, same era.
Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Ren, B., Qin, M., Tang, H., Wang, Y.: Spvit: Enabling faster vision transformers via soft token pruning. arXiv (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV (2021)
2021
Later among the works it cites.
Liu, Z., Wang, Y., Han, K., Zhang, W., Ma, S., Gao, W.: Post-training quantization for vision transformer. NeurIPS (2021)
2021
Later among the works it cites.
Mehta, S., Rastegari, M.: Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. In: ICLR (2021)
2021
Later among the works it cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)
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2019
Cited alongside, same era.
Wightman, R.: Pytorch image models (2019)
2019
Cited alongside, same era.
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: ICCV (2019)
2019
Cited alongside, same era.
Beyer, L., Hénaff, O.J., Kolesnikov, A., Zhai, X., Oord, A.v.d.: Are we done with imagenet? arXiv (2020)
2020
Cited alongside, same era.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV (2020)
2020
Cited alongside, same era.
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: Practical automated data augmentation with a reduced search space. In: CVPR (2020)
2020
Cited alongside, same era.
Feichtenhofer, C.: X3d: Expanding architectures for efficient video recognition. CVPR (2020)
2020
Cited alongside, same era.
Guo, Y., Codella, N.C., Karlinsky, L., Codella, J.V., Smith, J.R., Saenko, K., Rosing, T., Feris, R.: A broader study of cross-domain few-shot learning. In: ECCV (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Ridnik, T., Ben-Baruch, E., Noy, A., Zelnik-Manor, L.: Imagenet-21k pretraining for the masses. In: NeurIPS (2021)
2021
Later among the works it cites.
Riquelme, C., Puigcerver, J., Mustafa, B., Neumann, M., Jenatton, R., Pinto, A.S., Keysers, D., Houlsby, N.: Scaling vision with sparse mixture of experts. In: NeurIPS (2021)
2021
Later among the works it cites.
Shen, Z., Xing, E.: A fast knowledge distillation framework for visual recognition. arXiv (2021)
2021
Later among the works it cites.
Su, X., You, S., Xie, J., Zheng, M., Wang, F., Qian, C., Zhang, C., Wang, X., Xu, C.: Vitas: Vision transformer architecture search. arXiv (2021)
2021
Later among the works it cites.
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: ICML. PMLR (2021)
2021
Later among the works it cites.
Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: ICCV (2021)
2021
Later among the works it cites.
Wu, K., Peng, H., Chen, M., Fu, J., Chao, H.: Rethinking and improving relative position encoding for vision transformer. In: ICCV (2021)
2021
Later among the works it cites.
Xiao, T., Dollar, P., Singh, M., Mintun, E., Darrell, T., Girshick, R.: Early convolutions help transformers see better. NeurIPS (2021)
2021
Later among the works it cites.
Xu, W., Xu, Y., Chang, T., Tu, Z.: Co-scale conv-attentional image transformers. In: ICCV (2021)
2021
Later among the works it cites.
Yang, H., Yin, H., Molchanov, P., Li, H., Kautz, J.: Nvit: Vision transformer compression and parameter redistribution. In: arXiv (2021)
2021
Later among the works it cites.
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Tay, F.E., Feng, J., Yan, S.: Tokens-to-token vit: Training vision transformers from scratch on imagenet. ICCV (2021)
2021
Later among the works it cites.
Yuan, L., Chen, D., Chen, Y.L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., Liu, C., Liu, M., Liu, Z., Lu, Y., Shi, Y., Wang, L., Wang, J., Xiao, B., Xiao, Z., Yang, J., Zeng, M., Zhou, L., Zhang, P.: Florence: A new foundation model for computer vision. In: ArXiv (2021)
2021
Later among the works it cites.
Yun, S., Oh, S.J., Heo, B., Han, D., Choe, J., Chun, S.: Re-labeling imagenet: From single to multi-labels, from global to localized labels. In: CVPR (2021)
2021
Later among the works it cites.
Zhang, Q., bin Yang, Y.: Rest: An efficient transformer for visual recognition. In: NeurIPS (2021)
2021
Later among the works it cites.
Zhu, M., Tang, Y., Han, K.: Vision transformer pruning. In: KDD Workshop on Model Mining (2021)
2021
Later among the works it cites.
Bao, H., Dong, L., Wei, F.: Beit: Bert pre-training of image transformers. In: ICLR (2022)
2022
Closest in time.
Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., Liu, Z.: Mobile-former: Bridging mobilenet and transformer. In: CVPR (2022)
2022
Closest in time.
Gong, C., Wang, D., Li, M., Chen, X., Yan, Z., Tian, Y., liu, q., Chandra, V.: NASVit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training. In: ICLR (2022)
2022
Closest in time.
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al.: Swin transformer v2: Scaling up capacity and resolution. In: CVPR (2022)
2022
Closest in time.
Yu, S., Chen, T., Shen, J., Yuan, H., Tan, J., Yang, S., Liu, J., Wang, Z.: Unified visual transformer compression. In: ICLR (2022)
2022
Closest in time.
Zhai, X., Kolesnikov, A., Houlsby, N., Beyer, L.: Scaling vision transformers. In: CVPR (2022)
2022
Closest in time.
Zhang, J., Peng, H., Wu, K., Liu, M., Xiao, B., Fu, J., Yuan, L.: Minivit: Compressing vision transformers with weight multiplexing. In: CVPR (2022)
2022
Closest in time.
Zhou, W., Xu, C., McAuley, J.: Meta learning for knowledge distillation (2022)
2022
Closest in time.