We address the task of open-world class-agnostic object detection, i.e.,
detecting every object in an image by learning from a limited number of base
object classes. State-of-the-art RGB-based models suffer from overfitting the
training classes and often fail at detecting novel-looking objects. This is
because RGB-based models primarily rely on appearance similarity to detect
novel objects and are also prone to overfitting short-cut cues such as textures
and discriminative parts. To address these shortcomings of RGB-based object
detectors, we propose incorporating geometric cues such as depth and normals,
predicted by general-purpose monocular estimators. Specifically, we use the
geometric cues to train an object proposal network for pseudo-labeling
unannotated novel objects in the training set. Our resulting Geometry-guided
Open-world Object Detector (GOOD) significantly improves detection recall for
novel object categories and already performs well with only a few training
classes. Using a single "person" class for training on the COCO dataset, GOOD
surpasses SOTA methods by 5.0% AR@100, a relative improvement of 24%.

本文提出了一种基于几何线索的开放式物体检测方法 GOOD（Geometry-guided Open-world Object Detector），使用深度和法线等几何线索来伪标记未标记的新物体，从而显著提高新物体类别的检测召回率，通过在 COCO 数据集上使用单一的 “人” 类别进行训练，GOOD 在 AR@100 上相对于 SOTA 方法提高了 24％，达到了 5.0％的改进。

探索几何线索以检测开放世界中的物体

GOOD: Exploring Geometric Cues for Detecting Objects in an Open World

We consider the problem of discovering novel object categories in an image
collection. While these images are unlabelled, we also assume prior knowledge
of related but different image classes. We use such prior knowledge to reduce
the ambiguity of clustering, and improve the quality of the newly discovered
classes. Our contributions are twofold. The first contribution is to extend
Deep Embedded Clustering to a transfer learning setting; we also improve the
algorithm by introducing a representation bottleneck, temporal ensembling, and
consistency. The second contribution is a method to estimate the number of
classes in the unlabelled data. This also transfers knowledge from the known
classes, using them as probes to diagnose different choices for the number of
classes in the unlabelled subset. We thoroughly evaluate our method,
substantially outperforming state-of-the-art techniques in a large number of
benchmarks, including ImageNet, OmniGlot, CIFAR-100, CIFAR-10, and SVHN.

该研究考虑了在图像集合中发现新对象类别的问题，使用先前的相关但不同的图像类别的先验知识降低聚类的歧义，改善新发现类别的质量，其中使用了 Deep Embedded Clustering、传输学习、表示瓶颈、时间集成、一致性和类别估计等多种技巧，并在 ImageNet、OmniGlot、CIFAR-100、CIFAR-10 和 SVHN 等许多基准测试中显著优于现有技术。