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arXiv · 2312.02103

Learning Pseudo-Labeler beyond Noun Concepts for Open-Vocabulary Object Detection

Sunghun Kang·Junbum Cha·Jonghwan Mun·Byungseok Roh·Chang D. Yoo
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Abstract

Open-vocabulary object detection (OVOD) has recently gained significant attention as a crucial step toward achieving human-like visual intelligence. Existing OVOD methods extend target vocabulary from pre-defined categories to open-world by transferring knowledge of arbitrary concepts from vision-language pre-training models to the detectors. While previous methods have shown remarkable successes, they suffer from indirect supervision or limited transferable concepts. In this paper, we propose a simple yet effective method to directly learn region-text alignment for arbitrary concepts. Specifically, the proposed method aims to learn arbitrary image-to-text mapping for pseudo-labeling of arbitrary concepts, named Pseudo-Labeling for Arbitrary Concepts (PLAC). The proposed method shows competitive performance on the standard OVOD benchmark for noun concepts and a large improvement on referring expression comprehension benchmark for arbitrary concepts.

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Sunghun Kang, Junbum Cha, Jonghwan Mun, Byungseok Roh, Chang D. Yoo. 2023-12-04. Learning Pseudo-Labeler beyond Noun Concepts for Open-Vocabulary Object Detection. https://arxiv.org/abs/2312.02103

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