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

arXiv: https://arxiv.org/abs/2007.02355 · HTML: https://mlsanity.com/paper/2007.02355/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 20 of 20)

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- [Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2506.03440/) (2025)
- [HoughNet: Integrating near and long-range evidence for visual detection](https://mlsanity.com/paper/2104.06773/) (2021)
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- [Pyramid Correlation based Deep Hough Voting for Visual Object Tracking](https://mlsanity.com/paper/2110.07994/) (2021)
- [HR-RCNN: Hierarchical Relational Reasoning for Object Detection](https://mlsanity.com/paper/2110.13892/) (2021)
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- [YOGA: Deep Object Detection in the Wild with Lightweight Feature Learning and Multiscale Attention](https://mlsanity.com/paper/2307.05945/) (2023)
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- [With Great Context Comes Great Prediction Power: Classifying Objects via Geo-Semantic Scene Graphs](https://mlsanity.com/paper/2512.23024/) (2025)
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- [Efficient Neural Network Model Selection for Few-Class Application Datasets](https://mlsanity.com/paper/2606.19712/) (2026)

## Abstract (arXiv)

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 AP50), 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.