4.7 Review

Recent advances of few-shot learning methods and applications

Related references

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Article Computer Science, Information Systems

Fast target-aware learning for few-shot video object segmentation

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Summary: This study proposes a fast target-aware learning approach for few-shot video object segmentation (FSVOS). The approach quickly adapts to new video sequences from the first-frame annotation through a lightweight procedure. The proposed method achieves good performance on video object segmentation benchmarks by training the meta knowledge model offline and using the target model for online inference.

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Summary: This paper investigates few-shot text classification under a metric-based meta-learning framework, proposing to exploit interaction of query and support instances with an adapted bi-directional attention mechanism and leveraging cross-class knowledge for classification. Incorporating large margin loss to shorten intra-class distances and enlarge inter-class distances, the proposed solution outperforms state-of-the-art competitors, with bi-directional attention and cross-class knowledge contributing to the overall performance.

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Li-ion battery temperature estimation based on recurrent neural networks

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Summary: The study proposes using two types of recurrent neural networks to estimate the surface temperature of lithium-ion batteries under different ambient temperatures, showing that the two RNNs can achieve accurate real-time battery temperature estimation with a maximum temperature estimation error of approximately 0.75 degrees Celsius and a correlation coefficient greater than 0.95 between estimated and measured temperature curves.

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Learning to focus: cascaded feature matching network for few-shot image recognition

Mengting Chen et al.

Summary: The paper proposes a cascaded feature matching network (CFMN) to tackle the low-shot image recognition task, training a more fine-grained and adaptive deep distance metric using feature matching block and incorporating multi-scale information among compared images in different layers to boost recognition performance and improve generalization. Experiments on few-shot learning using standard datasets confirm the effectiveness of the proposed method, and the study of multi-label few-shot task on a new data split of the COCO dataset further demonstrates the superiority of the feature matching network in complex images.

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Binocular Mutual Learning for Improving Few-shot Classification

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Summary: The study introduces the Binocular Mutual Learning (BML) framework to achieve better performance gains by combining the global view and local view. The global view captures inter-class relationships and the local view focuses on matching positive pairs, while cross-view interaction promotes collaborative learning.

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Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning

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Summary: This paper introduces a new meta-learning framework MeTAL, which uses a task-adaptive loss function to effectively adapt to the needs of different tasks, demonstrating flexibility and effectiveness in few-shot learning scenarios.

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Relational Embedding for Few-Shot Classification

Dahyun Kang et al.

Summary: This method proposes to address few-shot classification by meta-learning what to observe and where to attend in a relational perspective. By leveraging self-correlational representation and cross-correlational attention modules, it consistently improves state-of-the-art methods on widely used few-shot classification benchmarks in experimental evaluation.

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Differentiable Architecture Search Based on Coordinate Descent

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Learning to Forget for Meta-Learning

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Principal characteristic networks for few-shot learning

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Multi-Level Semantic Feature Augmentation for One-Shot Learning

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Image Deformation Meta-Networks for One-Shot Learning

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Task Agnostic Meta-Learning for Few-Shot Learning

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Few-shot Learning via Saliency-guided Hallucination of Samples

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Finding Task-Relevant Features for Few-Shot Learning by Category Traversal

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Instance-Level Embedding Adaptation for Few-Shot Learning

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Low-shot Visual Recognition by Shrinking and Hallucinating Features

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AGA : Attribute-Guided Augmentation

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YFCC100M: The New Data in Multimedia Research

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One-Shot Learning of Scene Locations via Feature Trajectory Transfer

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Transient Attributes for High-Level Understanding and Editing of Outdoor Scenes

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The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding

Genevieve Patterson et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2014)

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One-shot learning of object categories

FF Li et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)