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Few-shot, fine-grained classification requires a model to learn subtle,fine-grained distinctions between different classes (e.g., birds) based on afew images alone. This requires a remarkable degree of invariance to pose,articulation and background. A solution is to use pose-normalizedrepresentations: first localize semantic parts in each image, and then describeimages by characterizing the appearance of each part. While suchrepresentations are out of favor for fully supervised classification, we showthat they are extremely effective for few-shot fine-grained classification.With a minimal increase in model capacity, pose normalization improves accuracybetween 10 and 20 percentage points for shallow and deep architectures,generalizes better to new domains, and is effective for multiple few-shotalgorithms and network backbones. Code is available athttps://github.com/Tsingularity/PoseNorm_Fewshot

Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition