Core Concepts
Attentive Feature Regularization (AFR) improves few-shot learning by enhancing feature representativeness and discriminability through semantic selection and attention-based calculations.
Abstract
Introduction to Few-Shot Learning and challenges.
Manifold regularization methods for classification improvement.
Proposal of Attentive Feature Regularization (AFR) for better feature representation.
Detailed explanation of AFR components: semantic selection, instance attention, channel attention.
Training and inference procedures with loss functions.
Experiments on popular FSL datasets showcasing the effectiveness of AFR.
Ablation study on semantic selection, different attentions, and training strategies.
Comparison with state-of-the-art FSL methods across various datasets.
Conclusion and future work.
Stats
Few-shot learning aims to classify novel objects with limited data.
Manifold regularization mixes samples from different categories for classification improvement.
Attentive Feature Regularization enhances feature representativeness and discriminability.
Quotes
"Empirical studies demonstrate the effectiveness of AFR in improving classifier performance."
"Our method achieves state-of-the-art performance on popular FSL datasets."