Fetching the paper…
Reading the bibliography…
Prior efforts in light-weight model development mainly centered on CNN and Transformer-based designs yet faced persistent challenges.
2014
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: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
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)
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2818–2826 (2016)
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1251–1258 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Schneider, N., Piewak, F., Stiller, C., Franke, U.: Regnet: Multimodal sensor registration using deep neural networks. In: 2017 IEEE intelligent vehicles symposium (IV). pp. 1803–1810. IEEE (2017)
2017
Earlier work this paper cites.
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence. vol. 31 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1492–1500 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)
2018
Earlier work this paper cites.
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV). pp. 116–131 (2018)
2018
Earlier work this paper cites.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4510–4520 (2018)
2018
Earlier work this paper cites.
Wang, Y., Xu, C., Qiu, J., Xu, C., Tao, D.: Towards evolutionary compression. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 2476–2485 (2018)
2018
Earlier work this paper cites.
Wang, Y., Xu, C., Xu, C., Xu, C., Tao, D.: Learning versatile filters for efficient convolutional neural networks. Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV). pp. 418–434 (2018)
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 113–123 (2019)
2019
Earlier work this paper cites.
Mehta, S., Rastegari, M., Shapiro, L., Hajishirzi, H.: Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9190–9200 (2019)
2019
Earlier work this paper cites.
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., Le, Q.V.: Mnasnet: Platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2820–2828 (2019)
2019
Cited alongside, same era.
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)
2019
Cited alongside, same era.
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: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6023–6032 (2019)
2019
Cited alongside, same era.
2022
Later among the works it cites.
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11976–11986 (2022)
2022
Later among the works it cites.
Nguyen, E., Goel, K., Gu, A., Downs, G., Shah, P., Dao, T., Baccus, S., Ré, C.: S4nd: Modeling images and videos as multidimensional signals with state spaces. Advances in neural information processing systems 35
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: Practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 702–703 (2020)
2020
Cited alongside, same era.
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1580–1589 (2020)
2020
Cited alongside, same era.
Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollár, P.: Designing network design spaces. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10428–10436 (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., Ré, C.: Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems 34
2021
Cited alongside, same era.
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: Proceedings of the IEEE/CVF international conference on computer vision. pp. 10012–10022 (2021)
2021
Cited alongside, same era.
Su, X., You, S., Xie, J., Zheng, M., Wang, F., Qian, C., Zhang, C., Wang, X., Xu, C.: Vitas: Vision transformer architecture search. In: European Conference on Computer Vision. pp. 139–157. Springer Nature Switzerland Cham (2022)
2022
Later among the works it cites.
Touvron, H., Cord, M., Jégou, H.: Deit iii: Revenge of the vit. In: European Conference on Computer Vision. pp. 516–533. Springer (2022)
2022
Later among the works it cites.
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., Li, Y.: Maxvit: Multi-axis vision transformer. In: European conference on computer vision. pp. 459–479. Springer (2022)
2022
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.: Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media 8
2022
Later among the works it cites.
Yang, C., Wang, Y., Zhang, J., Zhang, H., Wei, Z., Lin, Z., Yuille, A.: Lite vision transformer with enhanced self-attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11998–12008 (2022)
2022
Later among the works it cites.
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5728–5739 (2022)
2022
Later among the works it cites.
2023
Later among the works it cites.
Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: Efficientvit: Memory efficient vision transformer with cascaded group attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14420–14430 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.