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Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation Read chapter Read first chapter

2020 | OriginalPaper | Chapter

HoughNet: Integrating Near and Long-Range Evidence for Bottom-Up Object Detection

Authors : Nermin Samet, Samet Hicsonmez, Emre Akbas

Published in: Computer Vision – ECCV 2020

Publisher: Springer International Publishing

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Abstract

This paper presents HoughNet, a one-stage, anchor-free, voting-based, bottom-up object detection method. Inspired by the Generalized Hough Transform, HoughNet determines the presence of an object at a certain location by the sum of the votes cast on that location. Votes are collected from both near and long-distance locations based on a log-polar vote field. Thanks to this voting mechanism, HoughNet is able to integrate both near and long-range, class-conditional evidence for visual recognition, thereby generalizing and enhancing current object detection methodology, which typically relies on only local evidence. On the COCO dataset, HoughNet’s best model achieves 46.4 AP (and 65.1 \(AP_{50}\)), performing on par with the state-of-the-art in bottom-up object detection and outperforming most major one-stage and two-stage methods. We further validate the effectiveness of our proposal in another task, namely, “labels to photo” image generation by integrating the voting module of HoughNet to two different GAN models and showing that the accuracy is significantly improved in both cases. Code is available at https://​github.​com/​nerminsamet/​houghnet.

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previous chapter Temporal Complementary Learning for Video Person Re-identification
next chapter Graph Wasserstein Correlation Analysis for Movie Retrieval
Appendix
Available only for authorised users
Footnotes
1
We provide a step-by-step animation of the voting process at https://​shorturl.​at/​ilOP2.
 
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Metadata
Title
HoughNet: Integrating Near and Long-Range Evidence for Bottom-Up Object Detection
Authors
Nermin Samet
Samet Hicsonmez
Emre Akbas
Copyright Year
2020
Book
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58594-5

Electronic ISBN: 978-3-030-58595-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58595-2_25

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