The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available. When specialized in dense problems such as object detection, learning local rather than global information in images has proven to be more efficient. However, for unsupervised pretraining, the popular contrastive learning requires a large batch size and, therefore, a lot of resources. To address this problem, we are interested in transformer-based object detectors that have recently gained traction in the community with good performance and with the particularity of generating many diverse object proposals. In this work, we present Proposal Selection Contrast (ProSeCo), a novel unsupervised overall pretraining approach that leverages this property. ProSeCo uses the large number of object proposals generated by the detector for contrastive learning, which allows the use of a smaller batch size, combined with object-level features to learn local information in the images. To improve the effectiveness of the contrastive loss, we introduce the object location information in the selection of positive examples to take into account multiple overlapping object proposals. When reusing pretrained backbone, we advocate for consistency in learning local information between the backbone and the detection head. We show that our method outperforms state of the art in unsupervised pretraining for object detection on standard and novel benchmarks in learning with fewer data.

翻译：利用预训练深度神经网络是在少量数据条件下实现优异性能的有效途径。针对目标检测等密集预测任务，学习图像中的局部信息而非全局信息已被证明更为高效。然而，在无监督预训练中，主流的对比学习方法需要较大的批次尺寸，从而消耗大量计算资源。为解决该问题，我们聚焦于近期在学界备受关注的基于Transformer的目标检测器——该类模型不仅性能优异，且具有生成大量多样化目标提案的特性。本文提出提案选择对比（ProSeCo）这一新型无监督整体预训练方法，通过利用该特性，将检测器生成的大量目标提案用于对比学习，从而在采用更小批次尺寸的同时，结合目标级特征学习图像中的局部信息。为提升对比损失的效能，我们在正例选择过程中引入目标位置信息，以处理多个重叠目标提案。当复用预训练主干网络时，我们主张保持主干网络与检测头之间局部信息学习的一致性。实验表明，在标准及新型基准测试中，本方法在少样本学习场景下均优于现有最优的无监督预训练目标检测方案。