Multi-Level Semantic Feature Augmentation for One-Shot Learning
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- Type
- article
- Published
- 2018-04-15
- Cited by
- 276
- References
- 94
- Access
- Open access
- OpenAlex
- https://openalex.org/W2894906112
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52908669
Keywords
Semantics (computer science), Feature (linguistics), Semantic feature, Encoder, Feature vector
References
- Using the forest to see the trees: exploiting context for visual object detection and localization
- Caltech-256 Object Category Dataset
- Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
- The Caltech-UCSD Birds-200-2011 Dataset
- Learning to generate chairs with convolutional neural networks
- Fully convolutional networks for semantic segmentation
- Understanding deep image representations by inverting them
- Ontological supervision for fine grained classification of Street View storefronts
- Articulated pose estimation with tiny synthetic videos
- Auto-Encoding Variational Bayes
- Learning a kernel function for classification with small training samples
- What helps where – and why? Semantic relatedness for knowledge transfer
- Return of the Devil in the Details: Delving Deep into Convolutional Nets
- Do We Need More Training Data?
- The More You Know, the Less You Learn: From Knowledge Transfer to One-shot Learning of Object Categories
- Describing objects by their attributes
- Transfer learning for image classification with sparse prototype representations
- One-shot learning by inverting a compositional causal process
- One-shot learning of object categories
- Incremental learning of object detectors using a visual shape alphabet
Cited by
- Recent Advances in Open Set Recognition: A Survey
- Image Deformation Meta-Networks for One-Shot Learning
- A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning
- A hybrid approach with optimization-based and metric-based meta-learner for few-shot learning
- Few-Shot Learning for Domain-Specific Fine-Grained Image Classification
- A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation
- Hyperspectral Image Classification Based on Two-Phase Relation Learning Network
- Embodied One-Shot Video Recognition: Learning from Actions of a Virtual Embodied Agent
- MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
- One-Shot Image Classification by Learning to Restore Prototypes
- Continual Local Replacement for Few-shot Image Recognition
- PointAugment: An Auto-Augmentation Framework for Point Cloud Classification
- Continual Local Replacement for Few-shot Learning.
- TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
- An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning
- Adversarial Feature Hallucination Networks for Few-Shot Learning
- Unsupervised Few-shot Learning via Distribution Shift-based Augmentation
- Revisiting Metric Learning for Few-Shot Image Classification
- Multi-Scale Metric Learning for Few-Shot Learning
- Classification of Point Clouds for Indoor Components Using Few Labeled Samples
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