Match Them Up: Visually Explainable Few-shot Image Classification

• URL: http://arxiv.org/abs/2011.12527v1
• Date: Wed, 25 Nov 2020 05:47:35 GMT
• Title: Match Them Up: Visually Explainable Few-shot Image Classification
• Authors: Bowen Wang, Liangzhi Li, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara
• Abstract summary: Few-shot learning is usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories. In this paper, we reveal a new way to perform FSL for image classification, using visual representations from the backbone model and weights generated by a newly-emerged explainable classifier. Experimental results prove that the proposed method can achieve both good accuracy and satisfactory explainability on three mainstream datasets.
• Score: 27.867833878756553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories. However, there is no guarantee, especially for the latter part. This issue leads to the unknown nature of the inference process in most FSL methods, which hampers its application in some risk-sensitive areas. In this paper, we reveal a new way to perform FSL for image classification, using visual representations from the backbone model and weights generated by a newly-emerged explainable classifier. The weighted representations only include a minimum number of distinguishable features and the visualized weights can serve as an informative hint for the FSL process. Finally, a discriminator will compare the representations of each pair of the images in the support set and the query set. Pairs with the highest scores will decide the classification results. Experimental results prove that the proposed method can achieve both good accuracy and satisfactory explainability on three mainstream datasets.

Related papers

• PatchMix Augmentation to Identify Causal Features in Few-shot Learning [55.64873998196191]
  Few-shot learning aims to transfer knowledge learned from base with sufficient categories labelled data to novel categories with scarce known information. We propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency. We show that such an augmentation mechanism, different from existing ones, is able to identify the causal features.
  arXiv  Detail & Related papers  (2022-11-29T08:41:29Z)
• Semantic Cross Attention for Few-shot Learning [9.529264466445236]
  We propose a multi-task learning approach to view semantic features of label text as an auxiliary task. Our proposed model uses word-embedding representations as semantic features to help train the embedding network and a semantic cross-attention module to bridge the semantic features into the typical visual modal.
  arXiv  Detail & Related papers  (2022-10-12T15:24:59Z)
• Learning Primitive-aware Discriminative Representations for Few-shot Learning [28.17404445820028]
  Few-shot learning aims to learn a classifier that can be easily adapted to recognize novel classes with only a few labeled examples. We propose a Primitive Mining and Reasoning Network (PMRN) to learn primitive-aware representations. Our method achieves state-of-the-art results on six standard benchmarks.
  arXiv  Detail & Related papers  (2022-08-20T16:22:22Z)
• Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes [57.34346419239118]
  We propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC. This yields a new challenging benchmark for FSL RC, on which state of the art models show poor performance. We propose a novel classification scheme, in which the NOTA category is represented as learned vectors.
  arXiv  Detail & Related papers  (2021-04-17T08:16:49Z)
• Few-shot Image Classification with Multi-Facet Prototypes [48.583388368897126]
  We organize visual features into facets, which intuitively group features of the same kind. It is possible to predict facet importance from a pre-trained embedding of the category names. In particular, we propose an adaptive similarity measure, relying on predicted facet importance weights for a given set of categories.
  arXiv  Detail & Related papers  (2021-02-01T12:43:03Z)
• Learning and Evaluating Representations for Deep One-class Classification [59.095144932794646]
  We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks.
  arXiv  Detail & Related papers  (2020-11-04T23:33:41Z)
• Explainable Image Classification with Evidence Counterfactual [0.0]
  We introduce SEDC as a model-agnostic instance-level explanation method for image classification. For a given image, SEDC searches a small set of segments that, in case of removal, alters the classification. We compare SEDC(-T) with popular feature importance methods such as LRP, LIME and SHAP, and we describe how the mentioned importance ranking issues are addressed.
  arXiv  Detail & Related papers  (2020-04-16T08:02:48Z)
• TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification [50.358839666165764]
  We show that the Task-Adaptive Feature Sub-Space Learning (TAFSSL) can significantly boost the performance in Few-Shot Learning scenarios. Specifically, we show that on the challenging miniImageNet and tieredImageNet benchmarks, TAFSSL can improve the current state-of-the-art in both transductive and semi-supervised FSL settings by more than $5%$.
  arXiv  Detail & Related papers  (2020-03-14T16:59:17Z)
• Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning [0.5156484100374058]
  Few-shot learning aims to learn novel visual categories from very few samples. We propose a Self-Attention Based Complementary Module (SAC Module) to fulfill the weakly-supervised object localization. We also produce the activated masks for selecting discriminative deep descriptors for few-shot classification.
  arXiv  Detail & Related papers  (2020-03-02T14:07:05Z)
• AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning [112.95742995816367]
  We propose a new few-shot fewshot learning setting termed FSFSL. Under FSFSL, both the source and target classes have limited training samples. We also propose a graph convolutional network (GCN)-based label denoising (LDN) method to remove irrelevant images.
  arXiv  Detail & Related papers  (2020-02-28T10:34:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.