We present a novel approach to unsupervised learning for video object
segmentation (VOS). Unlike previous work, our formulation allows to learn dense
feature representations directly in a fully convolutional regime. We rely on
uniform grid sampling to extract a set of anchors and train our model to
disambiguate between them on both inter- and intra-video levels. However, a
naive scheme to train such a model results in a degenerate solution. We propose
to prevent this with a simple regularisation scheme, accommodating the
equivariance property of the segmentation task to similarity transformations.
Our training objective admits efficient implementation and exhibits fast
training convergence. On established VOS benchmarks, our approach exceeds the
segmentation accuracy of previous work despite using significantly less
training data and compute power.

本研究提出了一种新的无监督学习方法，其中使用密集特征表示直接进行卷积全制度的学习来进行视频对象分割。通过使用一个简单的规则化方案，该方法能够提高分割精度并达到快速的训练收敛。最终，该方法在标准的视频对象分割基准测试中取得了较高的精度。

视频分割的密集无监督学习

Dense Unsupervised Learning for Video Segmentation

Contrastive learning methods for unsupervised visual representation learning
have reached remarkable levels of transfer performance. We argue that the power
of contrastive learning has yet to be fully unleashed, as current methods are
trained only on instance-level pretext tasks, leading to representations that
may be sub-optimal for downstream tasks requiring dense pixel predictions. In
this paper, we introduce pixel-level pretext tasks for learning dense feature
representations. The first task directly applies contrastive learning at the
pixel level. We additionally propose a pixel-to-propagation consistency task
that produces better results, even surpassing the state-of-the-art approaches
by a large margin. Specifically, it achieves 60.2 AP, 41.4 / 40.5 mAP and 77.2
mIoU when transferred to Pascal VOC object detection (C4), COCO object
detection (FPN / C4) and Cityscapes semantic segmentation using a ResNet-50
backbone network, which are 2.6 AP, 0.8 / 1.0 mAP and 1.0 mIoU better than the
previous best methods built on instance-level contrastive learning. Moreover,
the pixel-level pretext tasks are found to be effective for pre-training not
only regular backbone networks but also head networks used for dense downstream
tasks, and are complementary to instance-level contrastive methods. These
results demonstrate the strong potential of defining pretext tasks at the pixel
level, and suggest a new path forward in unsupervised visual representation
learning. Code is available at https://github.com/zdaxie/PixPro.

本文提出了基于像素级别前提任务来学习密集特征表示的方法，并使用此方法在目标检测和语义分割数据集上超越了目前最好的方法