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Status: Unchecked ×Keyword: bounding box estimation ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Bottom-Up Object Detection by Grouping Extreme and Center Points

   Zhou, Zhuo and Krähenbühl · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2019

   The paper shows that detecting five keypoints per object and grouping them by geometric alignment can detect objects competitively, and can also give coarse and refined segmentation masks.

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   1. UncheckedA bottom-up detector that groups extreme and centre points reaches 43.2% bounding box AP on COCO test-dev, which the authors say matches region-based methods.“The proposed method performs on-par with the state-of-the-art region based detection methods, with a bounding box AP of 43.2% on COCO test-dev.”
   2. UncheckedExtreme points found by the detector can form a rough octagonal mask that scores 18.9% Mask AP on COCO, well above plain bounding boxes.“In addition, our estimated extreme points directly span a coarse octagonal mask, with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding boxes.”