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Revisiting Local Descriptor for Improved Few-Shot Classification

https://doi.org/10.1145/3511917

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?

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When Does Self-supervision Improve Few-Shot Learning?

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Embedding Propagation: Smoother Manifold for Few-Shot Classification

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Learning Representations for Automatic Colorization

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ImageNet Large Scale Visual Recognition Challenge

inferredcites →https://doi.org/10.1016/j.patcog.2021.107951openalex-snapshot

Unsupervised meta-learning for few-shot learning

inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot

Advances in neural information processing systems 7

inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot

Rethinking the Inception Architecture for Computer Vision

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Deep Residual Learning for Image Recognition

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Feature Pyramid Networks for Object Detection

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Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00760openalex-snapshot

Low-Shot Learning from Imaginary Data

inferredcites →https://doi.org/10.1109/cvpr.2019.00010openalex-snapshot

Edge-Labeling Graph Neural Network for Few-Shot Learning

inferredcites →https://doi.org/10.1109/cvpr.2019.00049openalex-snapshot

Meta-Transfer Learning for Few-Shot Learning

inferredcites →https://doi.org/10.1109/cvpr.2019.00743openalex-snapshot

Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning

inferredcites →https://doi.org/10.1109/cvpr.2019.00810openalex-snapshot

Skeleton-Based Action Recognition With Directed Graph Neural Networks

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Dense Classification and Implanting for Few-Shot Learning

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Meta-Learning With Differentiable Convex Optimization

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Task Agnostic Meta-Learning for Few-Shot Learning

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Adaptive Subspaces for Few-Shot Learning

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Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions

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Iterative Context-Aware Graph Inference for Visual Dialog

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Boosting Few-Shot Learning With Adaptive Margin Loss

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00792openalex-snapshot

Few-Shot Classification with Feature Map Reconstruction Networks

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Proceedings of IEEE International Conference on Computer Vision

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Unsupervised Visual Representation Learning by Context Prediction

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Mask R-CNN

inferredcites →https://doi.org/10.1109/iccv.2017.328openalex-snapshot

Low-Shot Visual Recognition by Shrinking and Hallucinating Features

inferredcites →https://doi.org/10.1109/iccv.2019.00620openalex-snapshot

Memory-Based Neighbourhood Embedding for Visual Recognition

inferredcites →https://doi.org/10.1109/iccv.2019.00676openalex-snapshot

PARN: Position-Aware Relation Networks for Few-Shot Learning

inferredcites →https://doi.org/10.1109/iccv48922.2021.00687openalex-snapshot

Self-Regulation for Semantic Segmentation

inferredcites →https://doi.org/10.1109/taes.1978.308564openalex-snapshot

IEEE Transactions on Pattern Analysis and Machine Intelligence

inferredcites →https://doi.org/10.1109/tip.2022.3140611openalex-snapshot

Video Moment Retrieval With Cross-Modal Neural Architecture Search

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Person Reidentification via Structural Deep Metric Learning

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Meta-Learning in Neural Networks: A Survey

inferredcites →https://doi.org/10.1109/tpami.2021.3085755openalex-snapshot

Context-Aware Graph Inference With Knowledge Distillation for Visual Dialog

inferredcites →https://doi.org/10.1109/wacv45572.2020.9093338openalex-snapshot

Charting the Right Manifold: Manifold Mixup for Few-shot Learning

inferredcites →https://doi.org/10.1145/3123266.3123391openalex-snapshot

Adaptively Attending to Visual Attributes and Linguistic Knowledge for Captioning

inferredcites →https://doi.org/10.1145/3323873.3325056openalex-snapshot

Annotating Objects and Relations in User-Generated Videos

inferredcites →https://doi.org/10.1145/3343031.3350870openalex-snapshot

Learning Using Privileged Information for Food Recognition

inferredcites →https://doi.org/10.1145/3375787openalex-snapshot

Deep Neighborhood Component Analysis for Visual Similarity Modeling

inferredcites →https://doi.org/10.1145/3394171.3413811openalex-snapshot

Memory-Augmented Relation Network for Few-Shot Learning

inferredcites →https://doi.org/10.1145/3404835.3462823openalex-snapshot

Deconfounded Video Moment Retrieval with Causal Intervention

inferredcites →https://doi.org/10.1145/3474085.3475218openalex-snapshot

Selective Dependency Aggregation for Action Classification

inferredcites →https://doi.org/10.1145/3474085.3475540openalex-snapshot

Interventional Video Relation Detection

inferredcites →https://doi.org/10.24963/ijcai.2021/471openalex-snapshot

Uncertainty-Aware Few-Shot Image Classification

inferredcites →https://doi.org/10.48550/arxiv.1707.09835openalex-snapshot

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

inferredcites →https://doi.org/10.48550/arxiv.1909.09157openalex-snapshot

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

inferredcites →https://doi.org/10.48550/arxiv.2004.10934openalex-snapshot

YOLOv4: Optimal Speed and Accuracy of Object Detection

inferredcites →https://doi.org/10.48550/arxiv.2007.15484openalex-snapshot

Learning from Few Samples: A Survey

inferredcites →https://doi.org/10.48550/arxiv.2008.02465openalex-snapshot

Few-shot Classification via Adaptive Attention

inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot

Caltech-UCSD Birds-200-2011 Dataset

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Dual representations network for few-shot learning based on local descriptor importance: integrating global and local features

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Dual selective knowledge transfer for few-shot classification

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ReNDCF: Relation network with dual-channel feature fusion for few-shot learning

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Contrastive prototype loss based discriminative feature network for few-shot learning

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Region-adaptive Concept Aggregation for Few-shot Visual Recognition

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Local descriptor-based spatial cross attention network for few-shot learning

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Hierarchical few-shot learning based on top-down correction mechanism with stop strategy

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GCDPL-Net: graph-based co-attention discriminative prototype learning networks for few-shot classification

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A review of weed image identification based on deep few-shot learning

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Bimodal semantic fusion prototypical network for few-shot classification

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A review of few-shot image classification: Approaches, datasets and research trends

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Hierarchical Knowledge Propagation and Distillation for Few-Shot Learning

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Learning discriminative prototypes: Adaptive relation-aware refinement and patch-level contextual feature reweighting for few-shot classification

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SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning

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Hard-positive-aware intra–inter prototype refinement for few-shot image classification

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Entropy calibrated prototype embedding for transductive few-shot learning

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A Progressive Multi-Scale Relation Network for Few-Shot Image Classification

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Few-Shot Classification Study for Prototype Fusion and Completion

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MMNP: Multi-Modal Negative Prototypes for Few-Shot Open-Set Recognition

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Sign Language Translation with Iterative Prototype

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AdptGL-CA: Adaptive Global-Local Metric Fusion With Contrastive Attention for Few-Shot Learning

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ADFormer: Generalizable Few-Shot Anomaly Detection With Dual CNN-Transformer Architecture

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Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer Based Approach

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Learning Visual Conditioning Tokens to Correct Domain Shift for Fully Test-Time Adaptation

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Task-Related Saliency for Few-Shot Image Classification

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Cross-channel spatial attention network for few-shot classification

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Multi-Agent Semi-Siamese Training for Long-Tail and Shallow Face Learning

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Multi-Content Interaction Network for Few-Shot Segmentation

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Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data

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ProtoRefine: Enhancing Prototypes with Similar Structure in Few-Shot Learning

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Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation

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Complementarily Learning Decoupled Category-Region-Aware Prototype for Few-Shot Classification

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Self-Regressive Prototype Refinement: Stepping from Local to Global Prototypes in Few-Shot Image Classification

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