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CornerNet introduces a novel method for detecting objects by focusing on top-left and bottom-right corners as paired keypoints, utilizing corner pooling to enhance localization accuracy. With state-of-the-art performance, this approach outshines traditional single-stage detectors.
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CornerNet: Detecting Objects as Paired Keypoints HeiLaw1,2 Jia Deng1,2 Princeton University1, *UniversityofMichigan2 *Work done at the University of Michigan
Object Detection Person Person
Two-Stage Detector [Girshick et al. CVPR’14] [He et al. ECCV’14][He et al. ICCV’17] [Cai & Vasconcelos, CVPR’18][Singh & Davis, CVPR’18] Region of Interest [Renetal.NIPS’15] Person 2nd Network 1st Network Person Region Pooling [Girshick,ICCV’15]
One-stage Detector [Redmon & Farhadi, CVPR’17] [Shen et al. ICCV’17][Liu et al. ECCV’16] [Fu et al. arXiv’17][Lin et al. ICCV’17] [Zhang et al. CVPR’18] Person Class Anchors ConvNet Person Class Anchors Background Class Anchors
Drawbacks of Anchor Boxes • Need a large number of anchors • A tiny fraction of anchors are positive examples • Slow down training [Lin et al. ICCV’17] • Extra hyperparameters – sizes and aspect ratios At least one anchor sufficiently overlaps with ground-truth
CornerNet: Detecting Objects as Paired Keypoints Whose Bottom-Right? Whose Top-Left? Bottom-Right Corner? Top-Left Corner? Class Class No Yes Person ConvNet Yes Person No Yes No Person No Yes Person
CornerNet: Detecting Objects as Paired Keypoints Whose Bottom-Right? Whose Top-Left? Bottom-Right Corner? Top-Left Corner? Class Class No Yes Person Yes Person No Yes No Person No Yes Person
CornerNet: Detecting Objects as Paired Keypoints Whose Bottom-Right? Whose Top-Left? Bottom-Right Corner? Top-Left Corner? Class Class No Yes Person Loss: distance Yes Person No Loss: similarity Yes No Person No Yes Person
Advantages of Detecting Corners Corner: 2 sides Center: 4 sides Detecting corner is easier than detecting center
Advantages of Detecting Corner w possible proposals corners h Represent possible proposals using only corners
Supervising Corner Detection Boxes sufficiently overlap with ground-truths Reducing penalties
Top-Left Corner Pooling max max feature maps
Top-Left Corner CornerNet Embedding Class Corner? Top-Left Corner Pooling Yes Person Yes Person Hourglass Network [Newell et al. ECCV’16] Yes Person Bottom-Right Corner Pooling Yes Person Bottom-Right Corner
[Lin et al. ECCV’14] One of the most challenging object detection benchmarks
Related Work Main difference: how grouping is done DeNet: [Tychsen-Smith & Petersson, CVPR’17] Two-stage; grouping in stage 2 by classifier Ours: One-stage; grouping by associative embedding Point Linking Network: [Wang et al. arXiv’17] Grouping by predicting pixel locations Ours: Grouping by associative embedding
Conclusions • CornerNet: Detecting objects as pairs of top-left and bottom-right corners • Corner pooling to help better localize corners • State-of-the-art performance among single-stage detectors
CornerNet: Detecting Objects as Paired Keypoints Poster P-4B-03 on the 2nd floor of the Philharmonie foyer Code Available At: https://github.com/umich-vl/CornerNet