Learning Pseudo-Labeler beyond Noun Concepts for Open-Vocabulary Object
  Detection
Sunghun Kang Junbum Cha Jonghwan Mun Byungseok Roh Chang D. Yoo
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.