Papers › MultiGrain: a unified image embedding for classes and instances
MultiGrain: a unified image embedding for classes and instances
Maxim Berman, Hervé Jégou, Andrea Vedaldi, Iasonas Kokkinos, Matthijs Douze
MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval. It builds on a standard classification trunk. The top of the network produces an embedding containing coarse and fine-grained information, so that images can be recognized based on the object class, particular object, or if they are distorted copies. Our joint training is simple: we minimize a cross-entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation, with no need for additional labels. A key component of MultiGrain is a pooling layer that takes advantage of high-resolution images with a network trained at a lower resolution. When fed to a linear classifier, the learned embeddings provide state-of-the-art classification accuracy. For instance, we obtain 79.4% top-1 accuracy with a ResNet-50 learned on Imagenet, which is a +1.8% absolute improvement over the AutoAugment method. When compared with the cosine similarity, the same embeddings perform on par with the state-of-the-art for image retrieval at moderate resolutions.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | MultiGrain PNASNet (500px) | Top 1 Accuracy | 83.6% | #407 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain PNASNet (450px) | Top 1 Accuracy | 83.2% | #447 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain SENet154 (450px) | Top 1 Accuracy | 83.1% | #462 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain SENet154 (400px) | Top 1 Accuracy | 83.0% | #473 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain SENet154 (500px) | Top 1 Accuracy | 82.7% | #506 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain PNASNet (400px) | Top 1 Accuracy | 82.6% | #519 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain PNASNet (300px) | Top 1 Accuracy | 81.3% | #648 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain R50-AA-500 | Top 1 Accuracy | 79.4% | #755 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain R50-AA-224 | Top 1 Accuracy | 78.2% | #845 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MultiGrain NASNet-A-Mobile (350px) | Top 1 Accuracy | 75.1% | #958 of 1060 | Archive leaderboard | report |
| Image Retrieval | INRIA Holidays | MultiGrain R50 @ 800 | Mean mAP | 92.5% | #1 of 2 | Archive leaderboard | report |
| Image Retrieval | INRIA Holidays | MultiGrain R50 @ 500 | Mean mAP | 91.8% | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: MultiGrain
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