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Cited by 997

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Feature Selective Anchor-Free Module for Single-Shot Object Detection

Open access
Zhu, Chenchen, He, Yihui, Savvides, Marios·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.00093
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Abstract

We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work independently or jointly with anchor-based branches. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy. Experimental results on the COCO detection track show that our FSAF module performs better than anchor-based counterparts while being faster. When working jointly with anchor-based branches, the FSAF module robustly improves the baseline RetinaNet by a large margin under various settings, while introducing nearly free inference overhead. And the resulting best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing single-shot detectors on COCO.

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Research works printed but not linked: 20

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Metadata

Authors
Zhu, C., He, Y., & Savvides, M.
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
840-849
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2019.00093
OpenAlex
W2919010068
Cited by
997
References
13
FWCI
45.12
Type
conference paper
Language
en
Open access
green
Licence

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Zhu, C., He, Y., & Savvides, M. (2019). Feature Selective Anchor-Free Module for Single-Shot Object Detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00093

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