HoughNet: Integrating near and long-range evidence for bottom-up object detection

ECCV 2020  ·  Nermin Samet, Samet Hicsonmez, Emre Akbas ·

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. read more

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Object Detection COCO minival HoughNet (HG-104) box AP 43.0 # 76
AP50 62.2 # 47
AP75 46.9 # 39
APS 25.5 # 42
APM 47.6 # 29
APL 55.8 # 47
Object Detection COCO minival HoughNet (HG-104, MS) box AP 46.1 # 43
AP50 64.6 # 26
AP75 50.3 # 21
APS 30.0 # 14
APM 48.8 # 19
APL 59.7 # 21
Object Detection COCO test-dev HoughNet (MS) box AP 46.4 # 80
AP50 65.1 # 68
AP75 50.7 # 62
APS 29.1 # 56
APM 48.5 # 69
APL 58.1 # 64
Hardware Burden None # 1
Operations per network pass None # 1

Methods