  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 other visual
detection tasks, namely, video object detection, instance segmentation, 3D
object detection and keypoint detection for human pose estimation, and an
additional ``labels to photo`` image generation task, where the integration of
our voting module consistently improves performance in all cases. Code is
available at \url{https://github.com/nerminsamet/houghnet}.
