Papers › CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
Filip Radenović, Giorgos Tolias, Ondřej Chum
Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks. However, this achievement is preceded by extreme manual annotation in order to perform either training from scratch or fine-tuning for the target task. In this work, we propose to fine-tune CNN for image retrieval from a large collection of unordered images in a fully automated manner. We employ state-of-the-art retrieval and Structure-from-Motion (SfM) methods to obtain 3D models, which are used to guide the selection of the training data for CNN fine-tuning. We show that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.
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c46a359503f53583 · report
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | Oxf105k | siaMAC+QE* | MAP | 77.9% | #6 of 9 | Archive leaderboard | report |
| Image Retrieval | Oxf5k | siaMAC+QE* | MAP | 82.9% | #7 of 11 | Archive leaderboard | report |
| Image Retrieval | Par106k | siaMAC+QE* | mAP | 78.3% | #6 of 7 | Archive leaderboard | report |
| Image Retrieval | Par6k | siaMAC+QE* | mAP | 85.6% | #5 of 7 | Archive leaderboard | report |
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