HoughNet：整合近距离和远距离证据，实现自自底向上的的目标检测
HoughNet: Integrating near and long-range evidence for bottom-up object detection
摘要：
本文提出了HoughNet，一个单阶段，无锚，投票为基础的，自自底向上的【形】的目标检测方法。受广义Hough变换的启发，HoughNet通过对某个位置的投票总和来确定该位置是否存在对象。基于对数极坐标投票场，从近距离和远距离位置收集投票。得益于这种投票机制，HoughNet能够整合近距离和远距离的、有类别条件的视觉识别证据，从而推广和增强当前的目标检测方法，这种方法通常只依赖于局部证据。在COCO数据集上，HoughNet的最佳模型达到了46.4 $AP$（和65.1 $AP_{50}$），在自自底向上的【形】的目标检测方面与最先进的方法不相上下，并且优于大多数主要的一阶段和两阶段方法。我们进一步验证了我们的建议在另一个任务，即“标签到照片”的图像生成的有效性，通过集成HoughNet的投票模块到两个不同的GAN模型，并显示在这两种情况下的准确性显着提高。代码可在https://github.com/nerminsamet/houghnet上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
