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

• URL: http://arxiv.org/abs/2212.11720v2
• Date: Sat, 24 Dec 2022 02:03:21 GMT
• Title: GOOD: Exploring Geometric Cues for Detecting Objects in an Open World
• Authors: Haiwen Huang, Andreas Geiger, Dan Zhang
• Abstract summary: State-of-the-art RGB-based models suffer from overfitting the training classes and often fail at detecting novel-looking objects. We propose incorporating geometric cues such as depth and normals, predicted by general-purpose monocular estimators. 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.
• Score: 33.25263418112558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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