Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning
<|reference_start|>Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning: 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{https://github.com/visresearch/mgc}.<|reference_end|>
@article{shen2024multi-grained,
    title={Multi-Grained Contrast for Data-Efficient Unsupervised Representation
  Learning},
    author={Chengchao Shen, Jianzhong Chen, Jianxin Wang},
    journal={arXiv preprint arXiv:2407.02014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02014},
    primaryClass={cs.CV}
}