Many object detectors focus on locating the center of the object they want to find. However, this leaves them with the secondary problem of determining the specifications of the bounding box, leading to undesirable solutions like anchor boxes. This paper directly detects the top left and the bottom right corners of objects independently, along with descriptors that allows to match the two later and form a complete bounding box. For this, a new pooling method, called corner pooling, is introduced.
OUTLINE:
0:00 – Intro & High-Level Overview
1:40 – Object Detection
2:40 – Pipeline I – Hourglass
4:00 – Heatmap & Embedding Outputs
8:40 – Heatmap Loss
10:55 – Embedding Loss
14:35 – Corner Pooling
20:40 – Experiments
Paper:
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Abstract:
We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.
Authors: Hei Law, Jia Deng
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