Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires a remarkable degree of invariance to pose, articulation and background. A solution is to use pose-normalized representations: first localize semantic parts in each image, and then describe images by characterizing the appearance of each part. While such representations are out of favor for fully supervised classification, we show that they are extremely effective for few-shot fine-grained classification. With a minimal increase in model capacity, pose normalization improves accuracy between 10 and 20 percentage points for shallow and deep architectures, generalizes better to new domains, and is effective for multiple few-shot algorithms and network backbones. Code is available at https://github.com/Tsingularity/PoseNorm_Fewshot
Code (1)
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning
Batch Normalization is a staple of computer vision models, including those employed in few-shot learning. Batch Normalization layers in convolutional neural networks are composed of a normalization step, followed by …
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot LearningTransfer LearningHierarchical Normalization for Robust Monocular Depth Estimation
In this paper, we address monocular depth estimation with deep neural networks. To enable training of deep monocular estimation models with various sources of datasets, state-of-the-art methods adopt image-level normaliz…
Depth EstimationMonocular Depth EstimationOne-Shot Fine-Grained Instance Retrieval
Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recogni…
Fine-Grained Visual CategorizationImage RetrievalRetrievalSimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features a…
Few-Shot Image ClassificationFew-Shot LearningGeneral ClassificationMeta-LearningRevisiting Batch Norm Initialization
Batch normalization (BN) is comprised of a normalization component followed by an affine transformation and has become essential for training deep neural networks. Standard initialization of each BN in a network sets the…