• Corpus ID: 67749964

Adaptive Cross-Modal Few-Shot Learning

@inproceedings{Xing2019AdaptiveCF,
  title={Adaptive Cross-Modal Few-Shot Learning},
  author={Chen Xing and Negar Rostamzadeh and Boris N. Oreshkin and Pedro H. O. Pinheiro},
  booktitle={NeurIPS},
  year={2019}
}
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance metric-based few-shot learning methods. Visual and semantic feature spaces have different structures by definition. For certain concepts, visual features might be richer and more discriminative than text ones. While for others, the inverse might be true. Moreover, when the support from visual information is limited in… 
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84 Citations
Highly Influential Citations
12
Background Citations
37
Methods Citations
27
Results Citations
2

84 Citations

Citation Type

Citation Type

Learning Class Prototypes Via Anisotropic Combination of Aligned Modalities for Few-Shot Learning
TLDR
It is argued that proper alignment method is important to improve the performance of cross-modal methods, as query data only has visual information in the few-shot learning tasks.
Multi-domain few-shot image recognition with knowledge transfer
Shaping Visual Representations with Attributes for Few-Shot Learning
TLDR
This work proposes attribute-shaped learning (ASL), which can normalize visual representations to predict attributes for query images and devise an attribute-visual attention module (AVAM), which utilizes attributes to generate more discriminative features.
Feature Transformation Network for Few-Shot Learning
TLDR
An attention-based affinity matrix is introduced to transform the semantical enhanced embedding vectors of query samples by associating the support set, thereby guiding the network to learn a sample representation that embodies higher semantic information in the target area.
Few-shot Learning with Contextual Cueing for Object Recognition in Complex Scenes
TLDR
This work proposes a Class-conditioned Context Attention Module (CCAM) that learns to weight the most important context elements while learning a particular concept and proposes a flexible gating mechanism to ground visual class representations in context semantics.
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
TLDR
This work proposes a variance-sensitive class of models that operate in a low-label regime and employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-Image net and tiered-ImageNet benchmarks.
Prototype Completion for Few-Shot Learning
TLDR
A novel prototype completion based meta-learning framework that introduces primitive knowledge and extracts representative features for seen attributes as priors and develops a Gaussian based prototype fusion strategy that fuses the mean-based and completed prototypes by exploiting the unlabeled samples.
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition
TLDR
The simple yet powerful Compositional Feature Aggregation module is presented as a weakly-supervised regularization for deep networks that can be conveniently plugged into existing models for end-to-end optimization while keeping the model size and computation cost nearly the same.
Pseudo Shots: Few-Shot Learning with Auxiliary Data
TLDR
A masking module is proposed that adjusts the features of auxiliary data to be more similar to those of the target classes and can improve accuracy by up to 18 accuracy points, particularly when the auxiliary data is semantically distant from the target task.
Learning from Few Samples: A Survey
TLDR
This paper provides a taxonomy for the techniques and categorize them as data-augmentation, embedding, optimization and semantics based learning for few-shot, one-shot and zero-shot settings, and describes the seminal work done in each category and discusses their approach towards solving the predicament of learning from few samples.
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