Abstract
The existing contrastive learning methods mainly focus on single-grained representation learning, e.g., part-level, object-level or scene-level ones, thus inevitably neglecting the transferability of representations on other granularity levels. In this paper, we aim to learn multi-grained representations, which can effectively describe the image on various granularity levels, thus improving generalization on extensive downstream tasks. To this end, we propose a novel Multi-Grained Contrast method (MGC) for unsupervised representation learning. Specifically, we construct delicate multi-grained correspondences between positive views and then conduct multi-grained contrast by the correspondences to learn more general unsupervised representations. Without pretrained on large-scale dataset, our method significantly outperforms the existing state-of-the-art methods on extensive downstream tasks, including object detection, instance segmentation, scene parsing, semantic segmentation and keypoint detection. Moreover, experimental results support the data-efficient property and excellent representation transferability of our method. The source code and trained weights are available at \url{this https URL}.
Abstract (translated)
目前，对比学习方法主要集中在单粒度表示学习，例如部分级别、对象级别或场景级别，从而忽略了其他粒度级别上表示的转移性。在本文中，我们的目标是学习多粒度表示，可以有效地描述图像在各个粒度级别上的特征，从而提高在广泛的下游任务上的泛化能力。为此，我们提出了一个名为多粒度对比（MGC）的无监督表示学习的新方法。具体来说，我们通过构建积极视角和对应关系之间的细腻多粒度对应关系，然后通过对应关系进行多粒度对比，学习更通用的无监督表示。在没有在大规模数据集上预训练的情况下，我们的方法在广泛的下游任务上显著超过了现有最先进的 methods，包括目标检测、实例分割、场景解析、语义分割和关键点检测。此外，实验结果证实了我们的方法具有数据有效的特性和出色的表示转移能力。源代码和训练后的权重可在此处访问：\url{这个链接}。
URL
https://arxiv.org/abs/2407.02014
