Built on

Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Original
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.
Fawzi, A., Frossard, P.: Manitest: Are classifiers really invariant? In: British Machine Vision Conference (BMVC). pp. 106.1–106.13 (2015)
2015
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. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Earlier work this paper cites.
Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 eighth international conference on quality of multimedia experience (QoMEX). pp. 1–6. IEEE (2016)
2016
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40
2017
Earlier work this paper cites.
Engstrom, L., Tsipras, D., Schmidt, L., Madry, A.: A rotation and a translation suffice: Fooling cnns with simple transformations. arXiv preprint arXiv:1712.02779 (2017)
Original
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
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.
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.

Similar

Hendrycks, D., Dietterich, T.G.: Benchmarking neural network robustness to common corruptions and surface variations. arXiv preprint arXiv:1807.01697 (2018)
Original
2018
Cited alongside, same era.
Rosenfeld, A., Zemel, R.S., Tsotsos, J.K.: The elephant in the room. arXiv preprint arXiv:1808.03305 (2018)
Original
2018
Cited alongside, same era.
Singh, B., Davis, L.S.: An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3578–3587 (2018)
2018
Cited alongside, same era.
Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L.: Dota: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3974–3983 (2018)
2018
Cited alongside, same era.

Then

Kang, D., Sun, Y., Hendrycks, D., Brown, T., Steinhardt, J.: Testing robustness against unforeseen adversaries. arXiv preprint arXiv:1908.08016 (2019)
Original
2019
Later among the works it cites.
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A.S., Bethge, M., Brendel, W.: Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 (2019)
Original
2019
Later among the works it cites.
Sundaramoorthi, G., Wang, T.E.: Translation insensitive cnns. arXiv preprint arXiv:1911.11238 (2019)
Original
2019
Later among the works it cites.
Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2. https://github.com/facebookresearch/detectron2 (2019)
2019
Later among the works it cites.

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2020

Shift Equivariance in Object Detection

Manfredi, Marco, Wang, Yu

Understand

Robustness to small image translations is a highly desirable property for object detectors.

However, recent works have shown that CNN-based classifiers are not shift invariant.
It is unclear to what extent this could impact object detection, mainly because of the architectural differences between the two and the dimensionality of the prediction space of modern detectors.
To assess shift equivariance of object detection models end-to-end, in this paper we propose an evaluation metric, built upon a greedy search of the lower and upper bounds of the mean average precision on a shifted image set.

Built on

Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Original
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.
Fawzi, A., Frossard, P.: Manitest: Are classifiers really invariant? In: British Machine Vision Conference (BMVC). pp. 106.1–106.13 (2015)
2015
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. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Earlier work this paper cites.
Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 eighth international conference on quality of multimedia experience (QoMEX). pp. 1–6. IEEE (2016)
2016
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40
2017
Earlier work this paper cites.
Engstrom, L., Tsipras, D., Schmidt, L., Madry, A.: A rotation and a translation suffice: Fooling cnns with simple transformations. arXiv preprint arXiv:1712.02779 (2017)
Original
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
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.
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.

Similar

Hendrycks, D., Dietterich, T.G.: Benchmarking neural network robustness to common corruptions and surface variations. arXiv preprint arXiv:1807.01697 (2018)
Original
2018
Cited alongside, same era.
Rosenfeld, A., Zemel, R.S., Tsotsos, J.K.: The elephant in the room. arXiv preprint arXiv:1808.03305 (2018)
Original
2018
Cited alongside, same era.
Singh, B., Davis, L.S.: An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3578–3587 (2018)
2018
Cited alongside, same era.
Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L.: Dota: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3974–3983 (2018)
2018
Cited alongside, same era.

Then

Kang, D., Sun, Y., Hendrycks, D., Brown, T., Steinhardt, J.: Testing robustness against unforeseen adversaries. arXiv preprint arXiv:1908.08016 (2019)
Original
2019
Later among the works it cites.
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A.S., Bethge, M., Brendel, W.: Benchmarking robustness in object detection: Autonomous driving when winter is coming. arXiv preprint arXiv:1907.07484 (2019)
Original
2019
Later among the works it cites.
Sundaramoorthi, G., Wang, T.E.: Translation insensitive cnns. arXiv preprint arXiv:1911.11238 (2019)
Original
2019
Later among the works it cites.
Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2. https://github.com/facebookresearch/detectron2 (2019)
2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Alcorn, M.A., Li, Q., Gong, Z., Wang, C., Mai, L., Ku, W.S., Nguyen, A.: Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

2019

Cited alongside, same era.

Azulay, A., Weiss, Y.: Why do deep convolutional networks generalize so poorly to small image transformations? Journal of Machine Learning Research 20

2019

Cited alongside, same era.

von Bernuth, A., Volk, G., Bringmann, O.: Simulating photo-realistic snow and fog on existing images for enhanced cnn training and evaluation. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). pp. 41–46. IEEE (2019)

2019

Cited alongside, same era.

Gu, K., Yang, B., Ngiam, J., Le, Q., Shlens, J.: Using videos to evaluate image model robustness. arXiv preprint arXiv:1904.10076 (2019)

Original

2019

Cited alongside, same era.

Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: A simple data processing method to improve robustness and uncertainty. In: International Conference on Learning Representations (2019)

2019

Cited alongside, same era.

Islam, M.A., Jia, S., Bruce, N.D.: How much position information do convolutional neural networks encode? In: International Conference on Learning Representations (2019)

2019

Cited alongside, same era.

Zhang, H., Wang, J.: Towards adversarially robust object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 421–430 (2019)

2019

Later among the works it cites.

Zhang, R.: Making convolutional networks shift-invariant again. In: International Conference on Machine Learning. pp. 7324–7334 (2019)

2019

Later among the works it cites.

Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. In: arXiv preprint arXiv:1904.07850 (2019)

Original

2019

Later among the works it cites.

Agarwal, V., Shetty, R., Fritz, M.: Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9690–9698 (2020)

2020

Closest in time.

Kayhan, O.S., Gemert, J.C.v.: On translation invariance in cnns: Convolutional layers can exploit absolute spatial location. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14274–14285 (2020)

2020

Closest in time.