Open-vocabulary Attribute Detection

• URL: http://arxiv.org/abs/2211.12914v1
• Date: Wed, 23 Nov 2022 12:34:43 GMT
• Title: Open-vocabulary Attribute Detection
• Authors: Mar\'ia A. Bravo, Sudhanshu Mittal, Simon Ging, Thomas Brox
• Abstract summary: This paper introduces the Open-Vocabulary Attribute Detection task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. Overall, the benchmark consists of 1.4 million annotations.
• Score: 38.5017012867974
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on object classes, whereas research on object attributes is limited due to the lack of a reliable attribute-focused evaluation benchmark. This paper introduces the Open-Vocabulary Attribute Detection (OVAD) task and the corresponding OVAD benchmark. The objective of the novel task and benchmark is to probe object-level attribute information learned by vision-language models. To this end, we created a clean and densely annotated test set covering 117 attribute classes on the 80 object classes of MS COCO. It includes positive and negative annotations, which enables open-vocabulary evaluation. Overall, the benchmark consists of 1.4 million annotations. For reference, we provide a first baseline method for open-vocabulary attribute detection. Moreover, we demonstrate the benchmark's value by studying the attribute detection performance of several foundation models. Project page https://ovad-benchmark.github.io/

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