Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer

• URL: http://arxiv.org/abs/2407.08460v2
• Date: Tue, 16 Jul 2024 09:28:59 GMT
• Title: Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer
• Authors: Tahira Shehzadi, Ifza, Didier Stricker, Muhammad Zeshan Afzal,
• Abstract summary: This paper presents a review of 27 cutting-edge developments in semi-supervised learning for object detection. It covers data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.
• Score: 12.042768320132694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a combination of a small labeled dataset and a larger, unlabeled dataset. This approach effectively reduces the dependence on large labeled datasets, which are often expensive and time-consuming to obtain. Initially, SSOD models encountered challenges in effectively leveraging unlabeled data and managing noise in generated pseudo-labels for unlabeled data. However, numerous recent advancements have addressed these issues, resulting in substantial improvements in SSOD performance. This paper presents a comprehensive review of 27 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers. We delve into the core components of semi-supervised learning and its integration into object detection frameworks, covering data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. Furthermore, we conduct a comparative analysis of various SSOD models, evaluating their performance and architectural differences. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.

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