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Improved Few-Shot Visual Classification

7 Dec 2019CVPR 2020 6arXiv:1912.03432archive 2025-07-28

Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal

Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date have focused on progressively more complex neural feature extractors and classifier adaptation strategies, as well as the refinement of the task definition itself. In this paper, we explore the hypothesis that a simple class-covariance-based distance metric, namely the Mahalanobis distance, adopted into a state of the art few-shot learning approach (CNAPS) can, in and of itself, lead to a significant performance improvement. We also discover that it is possible to learn adaptive feature extractors that allow useful estimation of the high dimensional feature covariances required by this metric from surprisingly few samples. The result of our work is a new "Simple CNAPS" architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset.

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Code

plai-group/simple-cnaps officialtfMIT report
peymanbateni/simple-cnaps mentioned in papermentioned on GitHubpytorchMIT report

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Tasks

ClassificationFew-Shot Image ClassificationFew-Shot LearningGeneral ClassificationImage ClassificationMeta-LearningObject Recognitionimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Image Classification Meta-Dataset Simple CNAPS Accuracy 69.86 #12 of 22 Archive leaderboard report
Few-Shot Image Classification Meta-Dataset Rank Simple CNAPS Mean Rank 3.45 #3 of 13 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (1-shot) Simple CNAPS + FETI Accuracy 63.5 #2 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (1-shot) Simple CNAPS Accuracy 37.1 #6 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (5-shot) Simple CNAPS + FETI Accuracy 83.1 #2 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 10-way (5-shot) Simple CNAPS Accuracy 56.7 #5 of 14 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) Simple CNAPS + FETI Accuracy 77.4 #19 of 105 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) Simple CNAPS Accuracy 53.2 #90 of 105 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) Simple CNAPS + FETI Accuracy 90.3 #11 of 95 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) Simple CNAPS Accuracy 70.8 #81 of 95 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (1-shot) Simple CNAPS + FETI Accuracy 57.1 #2 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (1-shot) Simple CNAPS Accuracy 48.1 #4 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (5-shot) Simple CNAPS + FETI Accuracy 78.5 #2 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 10-way (5-shot) Simple CNAPS Accuracy 70.2 #4 of 13 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (1-shot) Simple CNAPS + FETI Accuracy 71.4 #29 of 49 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (1-shot) Simple CNAPS Accuracy 63.0 #45 of 49 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (5-shot) Simple CNAPS + FETI Accuracy 86.0 #29 of 51 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (5-shot) Simple CNAPS Accuracy 80.0 #45 of 51 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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