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Spatial division networks for weakly supervised detection

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Abstract

With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and localization but lack of ability to present the detection explicitly. In this work, we propose a novel spatial division network, which is applied to detect bounding boxes only with weak supervision. The essence of our model is two innovative differentiable modules, determination network and parameterized division, which perform the spatial division in feature maps of classification networks. After training, the learned parameters of the spatial division would correspond to a set of predicted bounding box coordinates. To demonstrate the effectiveness of our model for multi-label classification and weakly supervised detection, we conduct extensive experiments on the multi-MNIST dataset. Experimental results show our spatial division networks can (1) help improve the accuracy of multi-label classification, (2) implement in an end-to-end way only with the image-level annotations, and (3) output accurate bounding box coordinate, thereby achieving multi-digits detection.

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Acknowledgements

This work was supported by National Key R&D Program of China under Grant 2018YFC0808304, and in part by the National Science Foundation of China under Grant 61976043 and Grant 61573081.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Yongsheng Liu, Wenyu Chen, Hong Qu & S. M. Hasan Mahmud

  2. China Coal Research Institute, Beijing, China

    Kebin Miao

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  1. Yongsheng Liu
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  2. Wenyu Chen
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  3. Hong Qu
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  4. S. M. Hasan Mahmud
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  5. Kebin Miao
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Correspondence to Hong Qu.

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Liu, Y., Chen, W., Qu, H. et al. Spatial division networks for weakly supervised detection. Neural Comput & Applic 33, 4965–4978 (2021). https://doi.org/10.1007/s00521-020-05257-z

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