用无标签数据训练模型，让目标检测更少依赖标注数据。
A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks
- 通过自监督学习在无标签数据上训练特征提取器。
- 性能超过ImageNet预训练模型，尤其适合目标检测任务。
- 模型更关注物体关键部分，特征表示更可靠鲁棒。
在人工智能快速发展的背景下，深度学习模型日益复杂庞大，但高质量标注数据的获取成为瓶颈。目标检测等复杂任务需大量人工标注，导致企业需投入大量人力或外包成本。本文提出一种自监督学习方法，利用无标签数据训练特征提取器，显著提升模型在少标注情况下的表现。实验表明，该方法所提模型超越了ImageNet预训练及专为检测设计的先进特征提取器。更重要的是，模型能聚焦于物体的关键特征，生成更优的特征表示，从而增强检测的可靠性与鲁棒性。
原文摘要 · Abstract (English)
In the fast-evolving field of artificial intelligence, where models are increasingly growing in complexity and size, the availability of labeled data for training deep learning models has become a significant challenge. Addressing complex problems like object detection demands considerable time and resources for data labeling to achieve meaningful results. For companies developing such applications, this entails extensive investment in highly skilled personnel or costly outsourcing. This research work aims to demonstrate that enhancing feature extractors can substantially alleviate this challenge, enabling models to learn more effective representations with less labeled data. Utilizing a self-supervised learning strategy, we present a model trained on unlabeled data that outperforms state-of-the-art feature extractors pre-trained on ImageNet and particularly designed for object detection tasks. Moreover, the results demonstrate that our approach encourages the model to focus on the most relevant aspects of an object, thus achieving better feature representations and, therefore, reinforcing its reliability and robustness.
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