• Ali, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., Verbeek, J., & Jegou H (2021). Xcit: Cross-covariance image transformers. InNeurIPS.

• Atito, S., Awais, M., & Kittler, J. (2021). Sit: Self-supervised vision transformer. arXiv preprintarXiv:2104.03602

• Baevski, A., Hsu, W.N., Xu, Q., Babu, A., Gu, J., & Auli, M. (2022). Data2vec: A general framework for self-supervised learning in speech, vision and language. InICML.

• Bao, H., Dong, L., Piao, S., & Wei, F. (2022). BEit: BERT pre-training of image transformers. InICLR.

• Bartz-Beielstein, T., Branke, J., Mehnen, J., & Mersmann, O. (2014).Evolutionary algorithms. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.

• Bello, I. (2021). Lambdanetworks: Modeling long-range interactions without attention. InICLR.

• Bertasius, G., Wang, H., & Torresani, L. (2021). Is space-time attention all you need for video understanding? InICML.

• Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., & Veit, A. (2021). Understanding robustness of transformers for image classification. InICCV.

• Bhowmik, P., Pantho, M. J. H., & Bobda, C. (2021). Bio-inspired smart vision sensor: Toward a reconfigurable hardware modeling of the hierarchical processing in the brain.Journal of Real-Time Image Processing,18, 157–174.

  Article  Google Scholar 

• Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. InTEC.

• Brest, J., Zamuda, A., Boskovic, B., Maucec, M. S., & Zumer, V. (2008). High-dimensional real-parameter optimization using self-adaptive differential evolution algorithm with population size reduction. InCEC.

• Brest, J., Zamuda, A., Fister, I., & Maučec, M. S. (2010). Large scale global optimization using self-adaptive differential evolution algorithm. InCEC.

• Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., & Agarwal, S. (2020). Language models are few-shot learners. InNeurIPS.

• Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In:ECCV.

• Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. InICCV.

• Chen, B., Li, P., Li, C., Li, B., Bai, L., Lin, C., Sun, M., Yan, J., & Ouyang, W. (2021). Glit: Neural architecture search for global and local image transformer. InICCV.

• Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021). Pre-trained image processing transformer. InCVPR.

• Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprintarXiv:2102.04306

• Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., Dai, J., Wang, J., Shi, J., Ouyang, W., Loy, C. C., & Lin, D. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprintarXiv:1906.07155

• Chen, M., Peng, H., Fu, J., & Ling, H. (2021). Autoformer: Searching transformers for visual recognition. InICCV.

• Chen, M., Wu, K., Ni, B., Peng, H., Liu, B., Fu, J., Chao, H., & Ling, H. (2021). Searching the search space of vision transformer. InNeurIPS.

• Chen, Q., Wu, Q., Wang, J., Hu, Q., Hu, T., Ding, E., & Cheng, J., Wang, J. (2022). Mixformer: Mixing features across windows and dimensions. InCVPR.

• Chen, T., Saxena, S., Li, L., Fleet, D. J., & Hinton, G. (2022). Pix2seq: A language modeling framework for object detection. InICLR.

• Chen, X., Ding, M., Wang, X., Xin, Y., Mo, S., Wang, Y., Han, S., Luo, P., Zeng, G., & Wang, J. (2023). Context autoencoder for self-supervised representation learning. InIJCV.

• Chen, X., Xie, S., & He, K. (2021). An empirical study of training self-supervised visual transformers. InICCV.

• Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., & Liu, Z. (2022). Mobile-former: Bridging mobilenet and transformer. InCVPR.

• Chen, Z., & Kang, L. (2005). Multi-population evolutionary algorithm for solving constrained optimization problems. InAIAI.

• Chen, Z., Zhu, Y., Zhao, C., Hu, G., Zeng, W., Wang, J., & Tang, M. (2021). Dpt: Deformable patch-based transformer for visual recognition. InACM MM.

• Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., & Girdhar, R. (2022). Masked-attention mask transformer for universal image segmentation. InCVPR.

• Cheng, B., Schwing, A., & Kirillov, A. (2021). Per-pixel classification is not all you need for semantic segmentation. InNeurIPS.

• Choromanski, K. M., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J. Q., Mohiuddin, A., Kaiser, L., Belanger, D. B., Colwell, L. J., & Weller, A. (2021). Rethinking attention with performers. InICLR.

• Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., & Shen, C. (2021). Twins: Revisiting the design of spatial attention in vision transformers. InNeurIPS.

• Chu, X., Tian, Z., Zhang, B., Wang, X., Wei, X., Xia, H., & Shen, C. (2023). Conditional positional encodings for vision transformers. InICLR.

• Coello, C. A. C., & Lamont, G. B. (2004).Applications of multi-objective evolutionary algorithms (Vol. 1). World Scientific.

• Cordonnier, J. B., Loukas, A., & Jaggi, M. (2020). On the relationship between self-attention and convolutional layers. InICLR.

• Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017). Deformable convolutional networks. InICCV.

• Das, S., & Suganthan, P. N. (2010). Differential evolution: A survey of the state-of-the-art.TEC.

• Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. InCVPR.

• Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. InNAACL.

• Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., & Guo, B. (2022). Cswin transformer: A general vision transformer backbone with cross-shaped windows. InCVPR.

• Dong, X., Bao, J., Zhang, T., Chen, D., Zhang, W., Yuan, L., Chen, D., Wen, F., Yu, N., & Guo, B. (2023). Peco: Perceptual codebook for Bert pre-training of vision transformers. InAAAI.

• Dong, Y., Cordonnier, J. B., & Loukas, A. (2021). Attention is not all you need: Pure attention loses rank doubly exponentially with depth. InICML.

• Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16\(\times \) 16 words: Transformers for image recognition at scale. InICLR.

• d’Ascoli, S., Touvron, H., Leavitt, M. L., Morcos, A. S., Biroli, G., & Sagun, L. (2021). Convit: Improving vision transformers with soft convolutional inductive biases. InICML.

• Fang, Y., Liao, B., Wang, X., Fang, J., Qi, J., Wu, R., Niu, J., & Liu, W. (2021). You only look at one sequence: Rethinking transformer in vision through object detection. InNeurIPS.

• Felleman, D. J., & Van Essen, D. C. (1991).Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex.

• Gao, P., Ma, T., Li, H., Lin, Z., Dai, J., & Qiao, Y. (2022). Mcmae: Masked convolution meets masked autoencoders. InNeurIPS.

• García-Martínez, C., & Lozano, M. (2008). Local search based on genetic algorithms. InAdvances in metaheuristics for hard optimization. Springer.

• Goyal, A., & Bengio, Y. (2022). Inductive biases for deep learning of higher-level cognition.Proceedings of the Royal Society A,478, 20210068.

  Article MathSciNet  Google Scholar 

• Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y., & Xu, C. (2022). Cmt: Convolutional neural networks meet vision transformers. InCVPR.

• Guo, M. H., Lu, C. Z., Liu, Z.N., Cheng, M. M., & Hu, S. M. (2023). Visual attention network. InCVM.

• Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021). Transformer in transformer. InNeurIPS.

• Hao, Y., Dong, L., Wei, F., & Xu, K. (2021). Self-attention attribution: Interpreting information interactions inside transformer. InAAAI.

• Hart, W. E., Krasnogor, N., & Smith, J. E. (2005). Memetic evolutionary algorithms. InRecent advances in memetic algorithms (pp. 3–27). Springer.

• Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., & Prasath, V. (2019). Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach.Information,10, 390.

  Article  Google Scholar 

• Hassani, A., Walton, S., Li, J., Li, S., & Shi, H. (2023). Neighborhood attention transformer. InCVPR.

• He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. InCVPR.

• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. InCVPR.

• He, R., Ravula, A., Kanagal, B., & Ainslie, J. (2020). Realformer: Transformer likes residual attention. arXiv preprintarXiv:2012.11747.

• Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., & Le, Q. V. (2019). Searching for mobilenetv3. InICCV.

• Huang, Z., Ben, Y., Luo, G., Cheng, P., Yu, G., & Fu, B. (2021). Shuffle transformer: Rethinking spatial shuffle for vision transformer. arXiv preprintarXiv:2106.03650.

• Hudson, D. A., & Zitnick, L. (2021). Generative adversarial transformers. InICML.

• Jiang, Y., Chang, S., & Wang, Z. (2021). Transgan: Two pure transformers can make one strong gan, and that can scale up. InNeurIPS.

• Jiang, Z.H., Hou, Q., Yuan, L., Zhou, D., Shi, Y., Jin, X., Wang, A., & Feng, J. (2021). All tokens matter: Token labeling for training better vision transformers. InNeurIPS.

• Katharopoulos, A., Vyas, A., Pappas, N., & Fleuret, F. (2020). Transformers are rnns: Fast autoregressive transformers with linear attention. InICML.

• Khare, V., Yao, X., & Deb, K. (2003). Performance scaling of multi-objective evolutionary algorithms. InEMO.

• Kim, J., Nguyen, D., Min, S., Cho, S., Lee, M., Lee, H., & Hong, S. (2022). Pure transformers are powerful graph learners. InNeurIPS.

• Kitaev, N., Kaiser, L., & Levskaya, A. (2020). Reformer: The efficient transformer. InICLR.

• Kolen, A., & Pesch, E. (1994). Genetic local search in combinatorial optimization.Discrete Applied Mathematics.

• Kumar, S., Sharma, V. K., & Kumari, R. (2014). Memetic search in differential evolution algorithm. arXiv preprintarXiv:1408.0101.

• Land, M. W. S. (1998).Evolutionary algorithms with local search for combinatorial optimization. University of California.

• Lee, Y., Kim, J., Willette, J., & Hwang, S. J. (2022). Mpvit: Multi-path vision transformer for dense prediction. InCVPR.

• Li, C., Tang, T., Wang, G., Peng, J., Wang, B., Liang, X., & Chang, X. (2021). Bossnas: Exploring hybrid cnn-transformers with block-wisely self-supervised neural architecture search. InICCV.

• Li, K., Wang, Y., Peng, G., Song, G., Liu, Y., Li, H., & Qiao, Y. (2022). Uniformer: Unified transformer for efficient spatial-temporal representation learning. InICLR.

• Li, K., Wang, Y., Zhang, J., Gao, P., Song, G., Liu, Y., Li, H., & Qiao, Y. (2023). Uniformer: Unifying convolution and self-attention for visual recognition.TPAMI.

• Li, X., Wang, L., Jiang, Q., & Li, N. (2021). Differential evolution algorithm with multi-population cooperation and multi-strategy integration.Neurocomputing,421, 285–302.

  Article  Google Scholar 

• Li, Y., Hu, J., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., & Ren, J. (2023). Rethinking vision transformers for mobilenet size and speed. InICCV.

• Li, Y., Zhang, K., Cao, J., Timofte, R., & Van Gool, L. (2021). Localvit: Bringing locality to vision transformers. arXiv preprintarXiv:2104.05707

• Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., & Timofte, R. (2021). Swinir: Image restoration using swin transformer. InICCV.

• Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. InECCV.

• Liu, J., & Lampinen, J. (2005). A fuzzy adaptive differential evolution algorithm.Soft Computing,9, 448–462.

  Article  Google Scholar 

• Liu, Y., Li, H., Guo, Y., Kong, C., Li, J., & Wang, S. (2022). Rethinking attention-model explainability through faithfulness violation test. InICML.

• Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., & Wei, F. (2022). Swin transformer v2: Scaling up capacity and resolution. InCVPR.

• Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. InICCV.

• Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization. InICLR.

• Lu, J., Mottaghi, R., & Kembhavi, A. (2021). Container: Context aggregation networks. InNeurIPS.

• Maaz, M., Shaker, A., Cholakkal, H., Khan, S., Zamir, S. W., Anwer, R. M., & Shahbaz Khan, F. (2023). Edgenext: Efficiently amalgamated cnn-transformer architecture for mobile vision applications. InECCVW.

• Mehta, S., & Rastegari, M. (2022). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. InICLR.

• Min, J., Zhao, Y., Luo, C., & Cho, M. (2022). Peripheral vision transformer. InNeurIPS.

• Moscato, P., et al. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms.Caltech Concurrent Computation Program, C3P Report,826, 1989.

  Google Scholar 

• Motter, B. C. (1993). Focal attention produces spatially selective processing in visual cortical areas v1, v2, and v4 in the presence of competing stimuli.Journal of Neurophysiology,70, 909–919.

  Article  Google Scholar 

• Nakashima, K., Kataoka, H., Matsumoto, A., Iwata, K., Inoue, N., & Satoh, Y. (2022). Can vision transformers learn without natural images? InAAAI.

• Neimark, D., Bar, O., Zohar, M., & Asselmann, D. (2021). Video transformer network. InICCV.

• Opara, K. R., & Arabas, J. (2019). Differential evolution: A survey of theoretical analyses.Swarm and Evolutionary Computation,44, 546–558.

  Article  Google Scholar 

• Padhye, N., Mittal, P., & Deb, K. (2013). Differential evolution: Performances and analyses. InCEC.

• Pan, X., Ge, C., Lu, R., Song, S., Chen, G., Huang, Z., & Huang, G. (2022). On the integration of self-attention and convolution. InCVPR.

• Pant, M., Zaheer, H., Garcia-Hernandez, L., Abraham, A., et al. (2020). Differential evolution: A review of more than two decades of research.Engineering Applications of Artificial Intelligence,90, 103479.

  Article  Google Scholar 

• Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., & Desmaison, A. (2019). Pytorch: An imperative style, high-performance deep learning library. InNeurIPS.

• Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. InICLR.

• Qiang, Y., Pan, D., Li, C., Li, X., Jang, R., & Zhu, D. (2022). Attcat: Explaining transformers via attentive class activation tokens. InNeurIPS.

• Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI.

• Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog.

• Raghu, M., Unterthiner, T., Kornblith, S., & Zhang, C. (2021). Dosovitskiy, A. Do vision transformers see like convolutional neural networks? InNeurIPS.

• Ren, S., Zhou, D., He, S., Feng, J., & Wang, X. (2022). Shunted self-attention via multi-scale token aggregation. InCVPR.

• Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., & Parikh, D. (2017). Batra, D. Grad-cam: Visual explanations from deep networks via gradient-based localization. InICCV.

• Shi, E. C., Leung, F. H., & Law, B. N. (2014). Differential evolution with adaptive population size. InICDSP.

• Si, C., Yu, W., Zhou, P., Zhou, Y., Wang, X., & Yan, S. (2022). Inception transformer. InNeurIPS.

• Sloss, A. N., & Gustafson, S. (2020). 2019 evolutionary algorithms review. InGenetic programming theory and practice XVII.

• Srinivas, A., Lin, T. Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021). Bottleneck transformers for visual recognition. InCVPR.

• Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces.Journal of Global Optimization 341–359

• Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. InICML.

• Thatipelli, A., Narayan, S., Khan, S., Anwer, R. M., Khan, F. S., & Ghanem, B. (2022). Spatio-temporal relation modeling for few-shot action recognition. InCVPR.

• Toffolo, A., & Benini, E. (2003). Genetic diversity as an objective in multi-objective evolutionary algorithms.Evolutionary Computation,11, 151–167.

  Article  Google Scholar 

• Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2021). Training data-efficient image transformers & distillation through attention. InICML.

• Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., & Jégou, H. (2021). Going deeper with image transformers. InICCV.

• Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022). Maxvit: Multi-axis vision transformer. InECCV.

• Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., & Patel, V. M. (2021). Medical transformer: Gated axial-attention for medical image segmentation. InMICCAI.

• Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B., & Shlens, J. (2021). Scaling local self-attention for parameter efficient visual backbones. InCVPR.

• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., & Polosukhin, I. (2017). Attention is all you need. InNeurIPS.

• Vikhar, P. A. (2016). Evolutionary algorithms: A critical review and its future prospects. InICGTSPICC.

• Wan, Z., Chen, H., An, J., Jiang, W., Yao, C., & Luo, J. (2022). Facial attribute transformers for precise and robust makeup transfer. InCACV.

• Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C., & Han, S. (2020). Hat: Hardware-aware transformers for efficient natural language processing. InACL.

• Wang, R., Chen, D., Wu, Z., Chen, Y., Dai, X., Liu, M., Jiang, Y. G., Zhou, L., & Yuan, L. (2022). Bevt: Bert pretraining of video transformers. InCVPR.

• Wang, S., Li, B., Khabsa, M., Fang, H., & Ma, H. (2020). Linformer: Self-attention with linear complexity. arXiv preprintarXiv:2006.04768

• Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. InICCV.

• Wang, W., Xie, E., Li, X., Fan, D. P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2022). Pvt v2: Improved baselines with pyramid vision transformer.CVM.

• Wang, W., Yao, L., Chen, L., Lin, B., Cai, D., He, X., & Liu, W. (2022). Crossformer: A versatile vision transformer hinging on cross-scale attention. InICLR.

• Wang, Y., Yang, Y., Bai, J., Zhang, M., Bai, J., Yu, J., Zhang, C., Huang, G., & Tong, Y. (2021). Evolving attention with residual convolutions. InICML.

• Wei, C., Fan, H., Xie, S., Wu, C. Y., Yuille, A., & Feichtenhofer, C. (2022). Masked feature prediction for self-supervised visual pre-training. InCVPR.

• Wightman, R. (2019). Pytorch image models.https://github.com/rwightman/pytorch-image-models

• Wightman, R., Touvron, H., & Jegou, H. (2021). Resnet strikes back: An improved training procedure in timm. InNeurIPSW.

• Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021). Cvt: Introducing convolutions to vision transformers. InICCV.

• Xia, Z., Pan, X., Song, S., Li, L.E., & Huang, G. (2022). Vision transformer with deformable attention. InCVPR.

• Xiao, T., Liu, Y., Zhou, B., Jiang, Y., & Sun, J. (2018). Unified perceptual parsing for scene understanding. InECCV.

• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). Segformer: Simple and efficient design for semantic segmentation with transformers. InNeurIPS.

• Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., & Hu, H. (2022). Simmim: A simple framework for masked image modeling. InCVPR.

• Xu, L., Yan, X., Ding, W., & Liu, Z. (2023). Attribution rollout: a new way to interpret visual transformer.JAIHC.

• Xu, M., Xiong, Y., Chen, H., Li, X., Xia, W., Tu, Z., & Soatto, S. (2021). Long short-term transformer for online action detection. InNeurIPS.

• Xu, W., Xu, Y., Chang, T., & Tu, Z. (2021). Co-scale conv-attentional image transformers. InICCV.

• Xu, Y., Zhang, Q., Zhang, J., & Tao, D. (2021). Vitae: Vision transformer advanced by exploring intrinsic inductive bias. InNeurIPS.

• Yang, C., Wang, Y., Zhang, J., Zhang, H., Wei, Z., Lin, Z., & Yuille, A. (2022). Lite vision transformer with enhanced self-attention. InCVPR.

• Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y., Wang, X., Feng, J., & Yan, S. (2022). Metaformer is actually what you need for vision. InCVPR.

• Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., & Wu, W. (2021). Incorporating convolution designs into visual transformers. InICCV.

• Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z. H., Tay, F. E., Feng, J., & Yan, S. (2021). Tokens-to-token vit: Training vision transformers from scratch on imagenet. InICCV.

• Yuan, L., Hou, Q., Jiang, Z., Feng, J., & Yan, S. (2022). Volo: Vision outlooker for visual recognition. InTPAMI.

• Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., & Wang, J. (2021). Hrformer: High-resolution vision transformer for dense predict. InNeurIPS.

• Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. H. (2022). Restormer: Efficient transformer for high-resolution image restoration. InCVPR.

• Zhang, J., Li, X., Li, J., Liu, L., Xue, Z., Zhang, B., Jiang, Z., Huang, T., Wang, Y., & Wang, C. (2023). Rethinking mobile block for efficient attention-based models. InICCV.

• Zhang, J., Xu, C., Li, J., Chen, W., Wang, Y., Tai, Y., Chen, S., Wang, C., Huang, F., & Liu, Y. (2021). Analogous to evolutionary algorithm: Designing a unified sequence model. InNeurIPS.

• Zhang, Q., Xu, Y., Zhang, J., & Tao, D. (2023). Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and beyond.IJCV.

• Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H., & Zhang, L. (2021). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. InCVPR.

• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019). Semantic understanding of scenes through the ade20k dataset. InIJCV.

• Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Hou, Q., & Feng, J. (2021). Deepvit: Towards deeper vision transformer. arXiv preprintarXiv:2103.11886

• Zhu, X., Hu, H., Lin, S., & Dai, J. (2019). Deformable convnets v2: More deformable, better results. InCVPR.

• Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2021). Deformable {detr}: Deformable transformers for end-to-end object detection. InICLR.

Download references