Papers › Low-shot Visual Recognition by Shrinking and Hallucinating Features
Low-shot Visual Recognition by Shrinking and Hallucinating Features
Bharath Hariharan, Ross Girshick
Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. To make progress on this foundational problem, we present a low-shot learning benchmark on complex images that mimics challenges faced by recognition systems in the wild. We then propose a) representation regularization techniques, and b) techniques to hallucinate additional training examples for data-starved classes. Together, our methods improve the effectiveness of convolutional networks in low-shot learning, improving the one-shot accuracy on novel classes by 2.3x on the challenging ImageNet dataset.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | ImageNet-FS (1-shot, novel) | SGM (ResNet-50) | Top-5 Accuracy (%) | 52.9 | #7 of 7 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (2-shot, novel) | SGM (ResNet-50) | Top-5 Accuracy (%) | 67.0 | #6 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (2-shot, novel) | SGM w/G (ResNet-50) | Top-5 Accuracy (%) | 64.9 | #8 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (5-shot, all) | SGM (ResNet-50) | Top-5 Accuracy (%) | 77.4 | #5 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | ImageNet-FS (5-shot, all) | SGM w/ G (ResNet-50) | Top-5 Accuracy (%) | 77.3 | #7 of 8 | 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.
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