Training image-based object detectors presents formidable challenges, as it
entails not only the complexities of object detection but also the added
intricacies of precisely localizing objects within potentially diverse and
noisy environments. However, the collection of imagery itself can often be
straightforward; for instance, cameras mounted in vehicles can effortlessly
capture vast amounts of data in various real-world scenarios. In light of this,
we introduce a groundbreaking method for training single-stage object detectors
through unsupervised/self-supervised learning.
Our state-of-the-art approach has the potential to revolutionize the labeling
process, substantially reducing the time and cost associated with manual
annotation. Furthermore, it paves the way for previously unattainable research
opportunities, particularly for large, diverse, and challenging datasets
lacking extensive labels.
In contrast to prevalent unsupervised learning methods that primarily target
classification tasks, our approach takes on the unique challenge of object
detection. We pioneer the concept of intra-image contrastive learning alongside
inter-image counterparts, enabling the acquisition of crucial location
information essential for object detection. The method adeptly learns and
represents this location information, yielding informative heatmaps. Our
results showcase an outstanding accuracy of \textbf{89.2\%}, marking a
significant breakthrough of approximately \textbf{15x} over random
initialization in the realm of unsupervised object detection within the field
of computer vision.

我们提出了一种通过无监督 / 自监督学习训练单阶段目标检测器的创新方法，该方法有潜力彻底改变标注过程，大大减少人工标注所需的时间和成本，并为之前难以实现的研究机会铺平了道路。与现有的主要针对分类任务的无监督学习方法不同，我们的方法承担了目标检测的独特挑战，开创了 intra-image 对比学习与 inter-image 对应的概念，能够获取目标检测所需的重要位置信息。该方法熟练地学习和表示这些位置信息，并生成信息丰富的热图。我们的结果展示了 89.2% 的卓越准确率，在计算机视觉领域的无监督目标检测领域突破性地提高了 15 倍，相比随机初始化。