Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting

@article{Kukleva2021GeneralizedAI,
  title={Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting},
  author={Anna Kukleva and Hilde Kuehne and Bernt Schiele},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={9000-9009}
}
Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier calibration across novel and base classes. In this work we propose a three-stage framework that allows to explicitly and effectively address these challenges. While the first phase learns base classes with many samples, the second phase learns a calibrated classifier for novel… 

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References

SHOWING 1-10 OF 56 REFERENCES

Incremental few-shot learning via vector quantization in deep embedded space

The proposed learning vector quantization in deep embedded space can be customized as a kernel smoother to handle incremental few-shot regression tasks and outperforms other state-of-the-art methods in incremental learning.

Few-Shot Class-Incremental Learning

This paper proposes the TOpology-Preserving knowledge InCrementer (TOPIC) framework, which mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples.

Learning to Compare: Relation Network for Few-Shot Learning

A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.

Relational Generalized Few-Shot Learning

This work proposes a graph-based framework that explicitly models relationships between all seen and novel classes in the joint label space of generalized few-shot learning and incorporates these inter-class relations using graph-convolution in order to embed novel class representations into the existing space of previously seen classes in a globally consistent manner.

Few-Shot Learning With Global Class Representations

This paper proposes to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples, and an effective sample synthesis strategy is developed to avoid overfitting.

Generalized Many-Way Few-Shot Video Classification

A simple 3D CNN baseline is developed, surpassing existing methods by a large margin and proposed to leverage weakly-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities, yielding further improvement.

Cognitively-Inspired Model for Incremental Learning Using a Few Examples

  • Ali AyubAlan R. Wagner
  • Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2020
This work proposes a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting when learning classes incrementally.

Incremental Few-Shot Learning with Attention Attractor Networks

A meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes, and it is demonstrated that the learned attractor network can help recognize novel classes while remembering old classes without the need to review the original training set.

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

This paper proposes a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddeddings that are task-specific and are discriminative.

A Baseline for Few-Shot Image Classification

This work performs extensive studies on benchmark datasets to propose a metric that quantifies the "hardness" of a few-shot episode and finds that using a large number of meta-training classes results in high few- shot accuracies even for a largeNumber of few-shots classes.
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