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Anchor-free Detection 专题

Anchor-free Detection 专题. Lejian Ren 20190512. 为什么要有 anchor. 直接预测坐标难以训练 F(x) =(x1, y1, x2, y2). 早期的 anchor-free: DenseBox. FCN 形式，输出 5 通道（ score + 4 个坐标变换） 对物体 size 差距较大的图像效果不好（ FPN? ）.

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Anchor-free Detection 专题

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  1. Anchor-free Detection专题 Lejian Ren 20190512

  2. 为什么要有anchor • 直接预测坐标难以训练 • F(x) =(x1, y1, x2, y2)

  3. 早期的anchor-free: DenseBox • FCN形式，输出5通道（score + 4个坐标变换） • 对物体size差距较大的图像效果不好（FPN?） Huang, Lichao, et al. "Densebox: Unifying landmark localization with end to end object detection." arXiv preprint arXiv:1509.04874 (2015).

  4. Denet 预测每个像素是什么类型的corner DeNet Tychsen-Smith, Lachlan, and Lars Petersson. "Denet: Scalable real-time object detection with directed sparse sampling." Proceedings of the IEEE International Conference on Computer Vision. 2017.

  5. CornerNet Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  6. CornerNet • 为什么不用anchor • anchor多 • 超参多 • 为什么用corner • Center比较难定位，需要考虑和四个边的关系 • Corner更高效 Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  7. CornerNet Heatmap参考了key point Embedded是一个d-1的数 Offset用来减小从feature map到原图的影响（共享） Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  8. CornerNet • 预测corner • 高斯 • Focal loss Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  9. CornerNet • Corner pooling • 为什么需要：物体的corner往往不在物体里面 Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  10. CornerNet • Grouping Corner Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  11. CornerNet Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  12. CornerNet Law, Hei, and Jia Deng. "Cornernet: Detecting objects as paired keypoints." Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  13. CornerNet-Lite • 加速 • CornerNet-Saccade • 减小输入尺寸 • 利用attention减少需要预测的点 • CornerNet-Squeeze • 修改backbone Law, Hei, et al. "CornerNet-Lite: Efficient Keypoint Based Object Detection." arXiv preprint arXiv:1904.08900 (2019).

  14. CornerNet-Lite • CornerNet-Saccade Law, Hei, et al. "CornerNet-Lite: Efficient Keypoint Based Object Detection." arXiv preprint arXiv:1904.08900 (2019).

  15. CornerNet-Lite Law, Hei, et al. "CornerNet-Lite: Efficient Keypoint Based Object Detection." arXiv preprint arXiv:1904.08900 (2019).

  16. CenterNet-1 • 预测key point • 预测长宽 X. Zhou, D. Wang, and P. Krähenbühl, “Objects as Points,” 2019.

  17. CenterNet-1 • Loss functions X. Zhou, D. Wang, and P. Krähenbühl, “Objects as Points,” 2019.

  18. CenterNet-1 X. Zhou, D. Wang, and P. Krähenbühl, “Objects as Points,” 2019.

  19. CenterNet-2 • CenterNet的缺点 • 原文认为：只预测corner无法很好的catch物体本身的信息 • 我认为：embedding没有区分度 • 从预测corner变成预测corner+center • 只有预测的center在预测的corner内部，才是正确的预测 Duan, Kaiwen, et al. "CenterNet: Keypoint Triplets for Object Detection." arXiv preprint arXiv:1904.08189 (2019).

  20. CenterNet-2 Duan, Kaiwen, et al. "CenterNet: Keypoint Triplets for Object Detection." arXiv preprint arXiv:1904.08189 (2019).

  21. CenterNet-2 • 如何判断center在corner内部（判定区域的设定） • 太大，precision低 • 太小，recall低 • 自适应 Duan, Kaiwen, et al. "CenterNet: Keypoint Triplets for Object Detection." arXiv preprint arXiv:1904.08189 (2019).

  22. CenterNet-2 • Center pooling • Cascade corner pooling Duan, Kaiwen, et al. "CenterNet: Keypoint Triplets for Object Detection." arXiv preprint arXiv:1904.08189 (2019).

  23. CenterNet-2 Duan, Kaiwen, et al. "CenterNet: Keypoint Triplets for Object Detection." arXiv preprint arXiv:1904.08189 (2019).

  24. RepPoints（representative points） • motivation： • 物体空间形状的可以通过几个reppoints来表示，通过学多个reppoints，就可以用其外接矩形来表示物体的框 Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, “RepPoints: Point Set Representation for Object Detection,” 2019.

  25. RepPoints 和deformable convolution 完美结合 Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, “RepPoints: Point Set Representation for Object Detection,” 2019.

  26. RepPoints • Pipeline： • Learning RepPoints • RepPoints refinement • Converting RepPoints to bounding box • Min-max function • Partial min-max function • Moment-based function (reppoints均值和二阶矩) Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, “RepPoints: Point Set Representation for Object Detection,” 2019.

  27. RepPoints Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, “RepPoints: Point Set Representation for Object Detection,” 2019.

  28. RepPoints Z. Yang, S. Liu, H. Hu, L. Wang, and S. Lin, “RepPoints: Point Set Representation for Object Detection,” 2019.

  29. 一点看法 • 为什么object detection没有一开始就anchor-free？ • 目前的anchor-free就是“anchor-free”吗？ • Anchor-free可能的发展方向

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