Methods > Computer Vision > Object Detection Models

ExtremeNet is a a bottom-up object detection framework that detects four extreme points (top-most, left-most, bottom-most, right-most) of an object. It uses a keypoint estimation framework to find extreme points, by predicting four multi-peak heatmaps for each object category. In addition, it uses one heatmap per category predicting the object center, as the average of two bounding box edges in both the x and y dimension. We group extreme points into objects with a purely geometry-based approach. We group four extreme points, one from each map, if and only if their geometric center is predicted in the center heatmap with a score higher than a pre-defined threshold, We enumerate all $O\left(n^{4}\right)$ combinations of extreme point prediction, and select the valid ones.

Source: Bottom-up Object Detection by Grouping Extreme and Center Points

Latest Papers

PAPER DATE
HoughNet: Integrating near and long-range evidence for bottom-up object detection
| Nermin SametSamet HicsonmezEmre Akbas
2020-07-05
Bottom-up Object Detection by Grouping Extreme and Center Points
| Xingyi ZhouJiacheng ZhuoPhilipp Krähenbühl
2019-01-23

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TASK PAPERS SHARE
Object Detection 2 66.67%
Image Generation 1 33.33%

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