论文标题
Houghnet：整合近距离和远程证据以进行自下而上的对象检测
HoughNet: Integrating near and long-range evidence for bottom-up object detection
论文作者
论文摘要
本文介绍了Houghnet，这是一种单阶段，无锚，基于投票的，自下而上的对象检测方法。受到广义的霍夫变换的启发，霍尼特通过在该地点投票的总和确定了某个位置的物体的存在。投票是根据对数极极投票领域的近距离和长距离地点收集的。由于这种投票机制，Houghnet能够整合近距离和远程的班级条件证据以进行视觉识别，从而概括和增强当前的对象检测方法，这通常仅依赖于本地证据。在可可数据集上，霍尼特的最佳型号可实现46.4 $ ap $（和65.1 $ ap_ {50} $），在自下而上的对象检测中与最先进的对象相当，超过了大多数主要的一阶段和两阶段方法。我们进一步验证了提案在另一项任务中的有效性，即，通过将HoughNet的投票模块集成到两个不同的GAN模型，并表明两种情况下的准确性都大大提高，“拍摄图像”图像生成。代码可在https://github.com/nerminsamet/houghnet上找到。
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.
