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Global contextual attention for pure regression object detection

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Abstract

Most object detection frameworks rely on rectangular bounding boxes and recognizing object instances individually. However, the bounding box provides only a coarse localization of objects and the context information between objects is not fully utilized, which result in a degradation of classification performance. In this paper, combining a lightweight contextual attention module with the representation of pure regression points, we present a novel context-based pure regression object detector. Moreover, a threshold filter mask module is designed to speed up the detector by removing a few insignificant points and keeping meaningful positions. Nonetheless, both of them do not require handcrafted clustering or post-processing steps and are easy to embed in networks. The proposed contextual attention module and threshold filter mask not only improve detection performance, but also promote training speed. We show through experiments that the proposed context-based pure regression detector can improve the representation of the regression points method about 1.5–1.8 AP on the COCO test-dev detection benchmark.

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Acknowledgements

The authors are very indebted to the anonymous referees for their critical comments and suggestions for the improvement of this paper. This work was supported by National Key Research and development Program of China (2021YFA1000102), and in part by the grants from the National Natural Science Foundation of China (Nos. 61673396, 61976245).

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  1. School of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266000, China

    Bingbing Fan, Mingwen Shao, Yunhao Li & Cunhe Li

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  1. Bingbing Fan
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  2. Mingwen Shao
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  3. Yunhao Li
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  4. Cunhe Li
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Correspondence to Mingwen Shao.

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Fan, B., Shao, M., Li, Y. et al. Global contextual attention for pure regression object detection. Int. J. Mach. Learn. & Cyber. 13, 2189–2197 (2022). https://doi.org/10.1007/s13042-022-01514-w

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  • DOI: https://doi.org/10.1007/s13042-022-01514-w

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