Detecting and localising unknown or Out-of-distribution (OOD) objects in any
scene can be a challenging task in vision. Particularly, in safety-critical
cases involving autonomous systems like automated vehicles or trains.
Supervised anomaly segmentation or open-world object detection models depend on
training on exhaustively annotated datasets for every domain and still struggle
in distinguishing between background and OOD objects. In this work, we present
a plug-and-play generalised framework - PRototype-based zero-shot OOD detection
Without Labels (PROWL). It is an inference-based method that does not require
training on the domain dataset and relies on extracting relevant features from
self-supervised pre-trained models. PROWL can be easily adapted to detect OOD
objects in any operational design domain by specifying a list of known classes
from this domain. PROWL, as an unsupervised method, outperforms other
supervised methods trained without auxiliary OOD data on the RoadAnomaly and
RoadObstacle datasets provided in SegmentMeIfYouCan (SMIYC) benchmark. We also
demonstrate its suitability for other domains such as rail and maritime scenes.

在本文中，我们提出了一种即插即用的广义框架 —— 无标签基于原型的零样本 OOD 检测（PROWL）。PROWL 是一种基于推理的方法，不需要在领域数据集上进行训练，依赖于从自监督预训练模型中提取相关特征。PROWL 可以轻松适应任何操作设计领域中的 OOD 对象，并且在 SegmentMeIfYouCan（SMIYC）基准提供的 RoadAnomaly 和 RoadObstacle 数据集上，作为一种无监督方法，它在没有辅助 OOD 数据进行训练的情况下优于其他有监督方法。我们还证明了它在其他领域（如铁路和海事场景）的适用性。

发现 Dino: 无监督检测外部分布对象的即插即用框架

Finding Dino: A plug-and-play framework for unsupervised detection of  out-of-distribution objects using prototypes

This study addresses the integration of diversity-based and uncertainty-based
sampling strategies in active learning, particularly within the context of
self-supervised pre-trained models. We introduce a straightforward heuristic
called TCM that mitigates the cold start problem while maintaining strong
performance across various data levels. By initially applying TypiClust for
diversity sampling and subsequently transitioning to uncertainty sampling with
Margin, our approach effectively combines the strengths of both strategies. Our
experiments demonstrate that TCM consistently outperforms existing methods
across various datasets in both low and high data regimes.

使用 TCM 方法，将多样性采样和不确定性采样策略整合到主动学习中，解决了冷启动问题，在低数据和高数据情况下均表现出卓越的性能。

主动学习中的多样性和不确定性的桥接与自监督预训练

Bridging Diversity and Uncertainty in Active learning with  Self-Supervised Pre-Training

We propose a novel unsupervised object localization method that allows us to
explain the predictions of the model by utilizing self-supervised pre-trained
models without additional finetuning. Existing unsupervised and self-supervised
object localization methods often utilize class-agnostic activation maps or
self-similarity maps of a pre-trained model. Although these maps can offer
valuable information for localization, their limited ability to explain how the
model makes predictions remains challenging. In this paper, we propose a simple
yet effective unsupervised object localization method based on representer
point selection, where the predictions of the model can be represented as a
linear combination of representer values of training points. By selecting
representer points, which are the most important examples for the model
predictions, our model can provide insights into how the model predicts the
foreground object by providing relevant examples as well as their importance.
Our method outperforms the state-of-the-art unsupervised and self-supervised
object localization methods on various datasets with significant margins and
even outperforms recent weakly supervised and few-shot methods.

我们提出了一种基于代表点选择的新型无监督目标定位方法，该方法利用自监督预训练模型解释模型的预测结果，无需额外微调。

无监督目标定位与代表点选择

Unsupervised Object Localization with Representer Point Selection

Self-supervised pre-trained models extract general-purpose representations
from data, and quantifying how reliable they are is crucial because many
downstream models use these representations as input for their own tasks. To
this end, we first introduce a formal definition of representation reliability:
the representation for a given test input is considered to be reliable if the
downstream models built on top of that representation can consistently generate
accurate predictions for that test point. It is desired to estimate the
representation reliability without knowing the downstream tasks a priori. We
provide a negative result showing that existing frameworks for uncertainty
quantification in supervised learning are not suitable for this purpose. As an
alternative, we propose an ensemble-based method for quantifying representation
reliability, based on the concept of neighborhood consistency in the
representation spaces across various pre-trained models. More specifically, the
key insight is to use shared neighboring points as anchors to align different
representation spaces. We demonstrate through comprehensive numerical
experiments that our method is capable of predicting representation reliability
with high accuracy.

提出一种基于邻域一致性的集成方法，用于估计自监督预训练模型的表征可靠性，实验结果表明该方法能够高精度地预测表征可靠性。