The recent enthusiasm for open-world vision systems show the high interest of
the community to perform perception tasks outside of the closed-vocabulary
benchmark setups which have been so popular until now. Being able to discover
objects in images/videos without knowing in advance what objects populate the
dataset is an exciting prospect. But how to find objects without knowing
anything about them? Recent works show that it is possible to perform
class-agnostic unsupervised object localization by exploiting self-supervised
pre-trained features. We propose here a survey of unsupervised object
localization methods that discover objects in images without requiring any
manual annotation in the era of self-supervised ViTs. We gather links of
discussed methods in the repository
this https URL

最近对开放式视觉系统的热情表明了社区在封闭词汇基准设置之外进行感知任务的高度兴趣。在不事先知道数据集中包含哪些对象的情况下，能够在图像 / 视频中发现对象是一个令人兴奋的前景。最近的研究表明，通过利用自我监督预训练特征，可以进行无类别无监督的对象定位。在自我监督 ViT 的时代，我们在此提出一种调查无监督对象定位方法，其能够在图像中发现对象而无需任何手动注释。我们在以下链接中汇总了所讨论方法的资源库：this https URL

自监督 ViTs 时代的无监督对象定位调查

Unsupervised Object Localization in the Era of Self-Supervised ViTs: A  Survey

The core idea of visual anomaly detection is to learn the normality from
normal images, but previous works have been developed specifically for certain
tasks, leading to fragmentation among various tasks: defect detection, semantic
anomaly detection, multi-class anomaly detection, and anomaly clustering. This
one-task-one-model approach is resource-intensive and incurs high maintenance
costs as the number of tasks increases. This paper presents SelFormaly, a
universal and powerful anomaly detection framework. We emphasize the necessity
of our off-the-shelf approach by pointing out a suboptimal issue with
fluctuating performance in previous online encoder-based methods. In addition,
we question the effectiveness of using ConvNets as previously employed in the
literature and confirm that self-supervised ViTs are suitable for unified
anomaly detection. We introduce back-patch masking and discover the new role of
top k-ratio feature matching to achieve unified and powerful anomaly detection.
Back-patch masking eliminates irrelevant regions that possibly hinder
target-centric detection with representations of the scene layout. The top
k-ratio feature matching unifies various anomaly levels and tasks. Finally,
SelFormaly achieves state-of-the-art results across various datasets for all
the aforementioned tasks.

SelFormaly 是一个通用且强大的异常检测框架，通过引入 back-patch masking 和 top k-ratio feature matching，它实现了统一且强大的异常检测，同时在各种数据集上取得了最先进的结果。