VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total recall: Automatic query expansion with a generative feature model for object retrieval. In: 2007 IEEE 11th International Conference on Computer Vision. pp. 1–8. IEEE (2007)

2007

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: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)

2009

Earlier work this paper cites.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15

2014

Earlier work this paper cites.

Clark, A.: Pillow (pil fork) documentation (2015), https://buildmedia.readthedocs.org/media/pdf/pillow/latest/pillow.pdf

2015

Earlier work this paper cites.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)

2015

Earlier work this paper cites.

Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167

Original

2015

Earlier work this paper cites.

Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision. pp. 1116–1124 (2015)

2015

Earlier work this paper cites.

Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: European conference on computer vision. pp. 646–661. Springer (2016)

2016

Earlier work this paper cites.

Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks (2016)

2016

Earlier work this paper cites.

Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 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.

Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-nms–improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision. pp. 5561–5569 (2017)

2017

Earlier work this paper cites.

Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4724–4733 (2017). https://doi.org/10.1109/CVPR.2017.502

2017

Earlier work this paper cites.

Dai, J., Qi, H., Xiong, 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)

2017

Earlier work this paper cites.

Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., Suleyman, M., Zisserman, A.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

Original

2017

Earlier work this paper cites.

Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection (2017)

2017

Earlier work this paper cites.

Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. arXiv preprint arXiv:1701.06548 (2017)

Original

2017

Earlier work this paper cites.

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)

2017

Earlier work this paper cites.

Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence (2017)

2017

Earlier work this paper cites.

Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. arXiv preprint arXiv:1703.01780 (2017)

Original

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.

Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1318–1327 (2017)

2017

Earlier work this paper cites.

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)

2018

Earlier work this paper cites.

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 (CVPR) (June 2018)

2018

Earlier work this paper cites.

Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018)

Original

2018

Earlier work this paper cites.

Hara, K., Kataoka, H., Satoh, Y.: Towards good practice for action recognition with spatiotemporal 3d convolutions. In: 2018 24th International Conference on Pattern Recognition (ICPR). pp. 2516–2521 (2018). https://doi.org/10.1109/ICPR.2018.8546325

2018

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.

Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: Enhancing learning and generalization capacities via ibn-net. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 464–479 (2018)

2018

Cited alongside, same era.

Radenović, F., Tolias, G., Chum, O.: Fine-tuning cnn image retrieval with no human annotation. IEEE transactions on pattern analysis and machine intelligence 41

2018

Cited alongside, same era.

Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6450–6459 (2018). https://doi.org/10.1109/CVPR.2018.00675

2018

Cited alongside, same era.

Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on Multimedia. pp. 274–282 (2018)

2018

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

Later among the works it cites.

Yang, C., Xu, Y., Shi, J., Dai, B., Zhou, B.: Temporal pyramid network for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

2020

Later among the works it cites.

Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., Manmatha, R., Li, M., Smola, A.: Resnest: Split-attention networks (2020)

2020

Later among the works it cites.

Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 13001–13008 (2020)

2020

Later among the works it cites.