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Self-EMD: Self-Supervised Object Detection without ImageNet

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arxiv 2011.13677 v3 pith:DWFSKQIG submitted 2020-11-27 cs.CV

Songtao Liu , Zeming Li , Jian Sun This is my paper · ORCID
classification cs.CV
keywords imageimagenetself-supervisedunlabeledcocodatasetdetectionlike
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In this paper, we propose a novel self-supervised representation learning method, Self-EMD, for object detection. Our method directly trained on unlabeled non-iconic image dataset like COCO, instead of commonly used iconic-object image dataset like ImageNet. We keep the convolutional feature maps as the image embedding to preserve spatial structures and adopt Earth Mover's Distance (EMD) to compute the similarity between two embeddings. Our Faster R-CNN (ResNet50-FPN) baseline achieves 39.8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet. More importantly, it can be further improved to 40.4% mAP with more unlabeled images, showing its great potential for leveraging more easily obtained unlabeled data. Code will be made available.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multiple Object Stitching for Unsupervised Representation Learning

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Multiple Object Stitching improves self-supervised representations by training on stitched multi-object images and reports gains on ImageNet, CIFAR, and COCO.

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    A co-evolutionary framework that alternates between chest X-ray abnormality detection and radiology report generation, using each task to refine the other's pseudo-labels, achieves state-of-the-art results on MS-CXR a...

  3. Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A detection pre-training framework that dynamically rebalances rare classes and adds dual reconstruction improves tail-class AP on COCO and LVIS by about 0.5 to 1.5 points in most configurations.

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