Practical Insights into Semi-Supervised Object Detection Approaches

• URL: http://arxiv.org/abs/2601.13380v1
• Date: Mon, 19 Jan 2026 20:31:15 GMT
• Title: Practical Insights into Semi-Supervised Object Detection Approaches
• Authors: Chaoxin Wang, Bharaneeshwar Balasubramaniyam, Anurag Sangem, Nicolais Guevara, Doina Caragea,
• Abstract summary: Semi-supervised object detection (SSOD) aims to improve detection performance by leveraging a large number of unlabeled images alongside a limited number of labeled images.<n>We compare three state-of-the-art SSOD approaches, including MixPL, Semi-DETR and Consistent-Teacher.<n>Our findings highlight the trade-offs between accuracy, model size, and latency, providing insights into which methods are best suited for low-data regimes.
• Score: 2.4538184328842574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning in data-scarce settings has recently gained significant attention in the research community. Semi-supervised object detection(SSOD) aims to improve detection performance by leveraging a large number of unlabeled images alongside a limited number of labeled images(a.k.a.,few-shot learning). In this paper, we present a comprehensive comparison of three state-of-the-art SSOD approaches, including MixPL, Semi-DETR and Consistent-Teacher, with the goal of understanding how performance varies with the number of labeled images. We conduct experiments using the MS-COCO and Pascal VOC datasets, two popular object detection benchmarks which allow for standardized evaluation. In addition, we evaluate the SSOD approaches on a custom Beetle dataset which enables us to gain insights into their performance on specialized datasets with a smaller number of object categories. Our findings highlight the trade-offs between accuracy, model size, and latency, providing insights into which methods are best suited for low-data regimes.

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