As vision-language models like CLIP are widely applied to zero-shot tasks and
gain remarkable performance on in-distribution (ID) data, detecting and
rejecting out-of-distribution (OOD) inputs in the zero-shot setting have become
crucial for ensuring the safety of using such models on the fly. Most existing
zero-shot OOD detectors rely on ID class label-based prompts to guide CLIP in
classifying ID images and rejecting OOD images. In this work we instead propose
to leverage a large set of diverse auxiliary outlier class labels as pseudo OOD
class text prompts to CLIP for enhancing zero-shot OOD detection, an approach
we called Outlier Label Exposure (OLE). The key intuition is that ID images are
expected to have lower similarity to these outlier class prompts than OOD
images. One issue is that raw class labels often include noise labels, e.g.,
synonyms of ID labels, rendering raw OLE-based detection ineffective. To
address this issue, we introduce an outlier prototype learning module that
utilizes the prompt embeddings of the outlier labels to learn a small set of
pivotal outlier prototypes for an embedding similarity-based OOD scoring.
Additionally, the outlier classes and their prototypes can be loosely coupled
with the ID classes, leading to an inseparable decision region between them.
Thus, we also introduce an outlier label generation module that synthesizes our
outlier prototypes and ID class embeddings to generate in-between outlier
prototypes to further calibrate the detection in OLE. Despite its simplicity,
extensive experiments show that OLE substantially improves detection
performance and achieves new state-of-the-art performance in large-scale OOD
and hard OOD detection benchmarks.

利用大规模和多样化的离群类别标签作为提示并通过离群样本生成模块增强零样本离群检测，实验结果表明 Outlier Label Exposure （OLE）对于大规模离群检测和难样本离群检测具有显著提升和创新性的性能。

利用异常标签曝光进行零样本领域外检测

Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure

In this paper, we raise a new issue on Unidentified Foreground Object (UFO)
detection in 3D point clouds, which is a crucial technology in autonomous
driving in the wild. UFO detection is challenging in that existing 3D object
detectors encounter extremely hard challenges in both 3D localization and
Out-of-Distribution (OOD) detection. To tackle these challenges, we suggest a
new UFO detection framework including three tasks: evaluation protocol,
methodology, and benchmark. The evaluation includes a new approach to measure
the performance on our goal, i.e. both localization and OOD detection of UFOs.
The methodology includes practical techniques to enhance the performance of our
goal. The benchmark is composed of the KITTI Misc benchmark and our additional
synthetic benchmark for modeling a more diverse range of UFOs. The proposed
framework consistently enhances performance by a large margin across all four
baseline detectors: SECOND, PointPillars, PV-RCNN, and PartA2, giving insight
for future work on UFO detection in the wild.

提出了一个针对 3D 点云中未知前景对象（UFO）检测的新问题，该问题是野外自动驾驶中至关重要的技术。通过建立一个包括评估协议、方法论和基准的新的 UFO 检测框架，解决了现有 3D 对象检测器在 3D 定位和超出分布（OOD）检测方面遇到的极大挑战。该框架在 KITTI Misc 基准和额外合成基准上通过四个基线检测器（SECOND、PointPillars、PV-RCNN、PartA2）跨越性能提升了一个较大的幅度，并为未来的野外 UFO 检测工作提供了新的见解。

UFO: 三维点云中的未知前景物体检测

UFO: Unidentified Foreground Object Detection in 3D Point Cloud

This thesis makes considerable contributions to the realm of machine
learning, specifically in the context of open-world scenarios where systems
face previously unseen data and contexts. Traditional machine learning models
are usually trained and tested within a fixed and known set of classes, a
condition known as the closed-world setting. While this assumption works in
controlled environments, it falls short in real-world applications where new
classes or categories of data can emerge dynamically and unexpectedly. To
address this, our research investigates two intertwined steps essential for
open-world machine learning: Out-of-distribution (OOD) Detection and Open-world
Representation Learning (ORL). OOD detection focuses on identifying instances
from unknown classes that fall outside the model's training distribution. This
process reduces the risk of making overly confident, erroneous predictions
about unfamiliar inputs. Moving beyond OOD detection, ORL extends the
capabilities of the model to not only detect unknown instances but also learn
from and incorporate knowledge about these new classes. By delving into these
research problems of open-world learning, this thesis contributes both
algorithmic solutions and theoretical foundations, which pave the way for
building machine learning models that are not only performant but also reliable
in the face of the evolving complexities of the real world.

这篇论文在机器学习领域做出了重要贡献，特别是在面对前所未见的数据和情境的开放世界情况下。通过研究开放世界机器学习中的 Out-of-distribution (OOD) Detection 和 Open-world Representation Learning (ORL) 两个关键步骤，该论文提出了算法解决方案和理论基础，为构建性能卓越且在不断变化的复杂实际世界中可靠的机器学习模型铺平了道路。