The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection
Open accessAbstract
Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead. The code is available at https://github.com/sfzhang15/ATSS.
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Citations
Cites
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“For the ablation study, we consider four typical object detection frameworks: Cascade Mask R-CNN [26, 6], ATSS [71], RepPoints v2 [12], and Sparse RCNN [52] in mmdetection [10].”
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“However, in recent years, if we take object detection as an example, researchers often use the quality and distribution of prediction output by the network, and then consider together with the ground truth to use some calculation and optimization methods to generate a reliable soft label [61, 8, 36, 99, 91, 44, 43, 90…”
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“es test-time augmentations including horizontal flip and multi-scale testing. Method Backbone TTA AP AP 50 AP 75 AP S AP M AP L FCOS (Tian et al., 2019) ResNeXt-101 44.7 64.1 48.4 27.6 47.5 55.6 ATSS (Zhang et al., 2020) ResNeXt-101 + DCN X 50.7 68.9 56.3 33.2 52.9 62.4 TSD (Song et al., 2020) SENet154 + DCN X 51.2 71.…”
InfluentialYOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
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“Compared with the ATSS, SimOTA can increase AP by 2.0%, and TAL brings 0.5% higher AP than SimOTA.”
BackgroundMethodInfluentialTOOD: Task-aligned One-stage Object Detection
Feng, C., Zhong, Y., Gao, Y., Scott, M. R., & Huang, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00349 · Cited by 1,668
“Recent one-stage object detectors attempted to predict consistent outputs of the two separate tasks, by focusing on the center of an object [3, 10, 27, 30].”
BackgroundMethodResultInfluentialSparse R-CNN: End-to-End Object Detection with Learnable Proposals
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“The spatial prior is widely used in the training phase [21, 22, 58, 53, 46, 50].”
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“Under the same configuration in the previous section, we replace ATSS with the orig- inal SimOTA and our modified SimOTA.”
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“Inthiswork,FCOSandATSS[106]wereemployedas thebaselinedetectors.”
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Metadata
- Authors
- Zhang, S., Chi, C., Yao, Y., Lei, Z., & Li, S. Z.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 9756-9765
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035396860
- Cited by
- 2,354
- References
- 18
- FWCI
- 87.76
- Type
- conference paper
- Language
- en
- Open access
- green
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