Transformers trained with self-supervised learning using self-distillation
loss (DINO) have been shown to produce attention maps that highlight salient
foreground objects. In this paper, we demonstrate a graph-based approach that
uses the self-supervised transformer features to discover an object from an
image. Visual tokens are viewed as nodes in a weighted graph with edges
representing a connectivity score based on the similarity of tokens. Foreground
objects can then be segmented using a normalized graph-cut to group
self-similar regions. We solve the graph-cut problem using spectral clustering
with generalized eigen-decomposition and show that the second smallest
eigenvector provides a cutting solution since its absolute value indicates the
likelihood that a token belongs to a foreground object. Despite its simplicity,
this approach significantly boosts the performance of unsupervised object
discovery: we improve over the recent state of the art LOST by a margin of
6.9%, 8.1%, and 8.1% respectively on the VOC07, VOC12, and COCO20K. The
performance can be further improved by adding a second stage class-agnostic
detector (CAD). Our proposed method can be easily extended to unsupervised
saliency detection and weakly supervised object detection. For unsupervised
saliency detection, we improve IoU for 4.9%, 5.2%, 12.9% on ECSSD, DUTS,
DUT-OMRON respectively compared to previous state of the art. For weakly
supervised object detection, we achieve competitive performance on CUB and
ImageNet.

本文提出了一种基于自监督学习训练的 Transformer 模型特征的基于图的对象发现方法，在图中进行规范化的图割以分组具有相似性区域的前景物体。该方法可以显著提高无监督对象发现的性能，也可以扩展到无监督凸显性检测和弱监督对象检测领域。

使用归一化切割的自监督 Transformers 进行无监督目标发现

Self-Supervised Transformers for Unsupervised Object Discovery using  Normalized Cut

Recent progress in self-supervised learning has demonstrated promising
results in multiple visual tasks. An important ingredient in high-performing
self-supervised methods is the use of data augmentation by training models to
place different augmented views of the same image nearby in embedding space.
However, commonly used augmentation pipelines treat images holistically,
ignoring the semantic relevance of parts of an image-e.g. a subject vs. a
background-which can lead to the learning of spurious correlations. Our work
addresses this problem by investigating a class of simple, yet highly effective
"background augmentations", which encourage models to focus on
semantically-relevant content by discouraging them from focusing on image
backgrounds. Through a systematic investigation, we show that background
augmentations lead to substantial improvements in performance across a spectrum
of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety
of tasks, e.g. $\sim$+1-2% gains on ImageNet, enabling performance on par with
the supervised baseline. Further, we find the improvement in limited-labels
settings is even larger (up to 4.2%). Background augmentations also improve
robustness to a number of distribution shifts, including natural adversarial
examples, ImageNet-9, adversarial attacks, ImageNet-Renditions. We also make
progress in completely unsupervised saliency detection, in the process of
generating saliency masks used for background augmentations.

通过引入背景增强技术，加强自监督学习中对物体的语义关注， 系统调研发现其在多个自监督学习任务中均有大幅提升效果，并成为有监督学习的有力竞争者，并在无监督显著性检测方面取得进展。