  Recently, self-supervised methods show remarkable achievements in image-level
representation learning. Nevertheless, their image-level self-supervisions lead
the learned representation to sub-optimal for dense prediction tasks, such as
object detection, instance segmentation, etc. To tackle this issue, several
recent self-supervised learning methods have extended image-level single
embedding to pixel-level dense embeddings. Unlike image-level representation
learning, due to the spatial deformation of augmentation, it is difficult to
sample pixel-level positive pairs. Previous studies have sampled pixel-level
positive pairs using the winner-takes-all among similarity or thresholding
warped distance between dense embeddings. However, these naive methods can be
struggled by background clutter and outliers problems. In this paper, we
introduce Hough Contrastive Learning (HoughCL), a Hough space based method that
enforces geometric consistency between two dense features. HoughCL achieves
robustness against background clutter and outliers. Furthermore, compared to
baseline, our dense positive pairing method has no additional learnable
parameters and has a small extra computation cost. Compared to previous works,
our method shows better or comparable performance on dense prediction
fine-tuning tasks.
