Dataset Preparation for Arbitrary Object Detection: An Automatic Approach based on Web Information in English
PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023(2023)
摘要
Automatic dataset preparation can help users avoid labor-intensive and costly manual data annotations. The difficulty in preparing a high-quality dataset for object detection involves three key aspects: relevance, naturality, and balance, which are not addressed by existing works. In this paper, we leverage information from the web, and propose a fully-automatic dataset preparation mechanism without any human annotation, which can automatically prepare a high-quality training dataset for the detection task with English text terms describing target objects. It contains three key designs, i.e., keyword expansion, data de-noising, and data balancing. Our experiments demonstrate that the object detectors trained with auto-prepared data are comparable to those trained with benchmark datasets and outperform other baselines. We also demonstrate the effectiveness of our approach in several more challenging real-world object categories that are not included in the benchmark datasets.
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关键词
dataset preparation,web information retrieval,object detection
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