Few-Shot Image Recognition by Predicting Parameters from Activations

Qiao, Siyuan; Liu, Chenxi; Shen, Wei; Yuille, Alan

doi:10.1109/cvpr.2018.00755

Cited by 497 publications

(377 citation statements)

References 18 publications

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“…Few-shot learning. There is a broad array of few-shot learning approaches, including, among many: gradient descent-based approaches [1,11,38,44], which learn how to rapidly adapt a model to a given few-shot recognition task via a small number of gradient descent iterations; metric learning based approaches that learn a distance metric be-tween a query, i.e., test image, and a set of support images, i.e., training images, of a few-shot task [26,52,54,56,58]; methods learning to map a test example to a class label by accessing memory modules that store training examples for that task [12,25,34,37,49]; approaches that learn how to generate the weights of a classifier [13,16,42,43] or of a multi-layer neural network [3,18,19,57] for the new classes given the few available training data for each of them; methods that "hallucinate" additional examples of a class from a reduced amount of data [20,56].…”

Section: Related Workmentioning

confidence: 99%

Boosting Few-Shot Visual Learning With Self-Supervision

Gidaris

Bursuc

Komodakis

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

367

221

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Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as an auxiliary task in a few-shot learning pipeline, enabling feature extractors to learn richer and more transferable visual representations while still using few annotated samples. Through self-supervision, our approach can be naturally extended towards using diverse unlabeled data from other datasets in the few-shot setting. We report consistent improvements across an array of architectures, datasets and self-supervision techniques.

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Section: Related Workmentioning

confidence: 99%

Boosting Few-Shot Visual Learning With Self-Supervision

Gidaris

Bursuc

Komodakis

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

367

221

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“…Experimental results on miniImageNet are shown in Table 1, where we can see that our model achieves state-of-the-art performance with Con-vNet backbone and competitive results with ResNet architecture. We re-implement ProtoNet as our baseline with the simple pre-train strategy proposed by [23], and achieve better performance than previously reported ones in [29]. Taking ConvNet as an example, we get 51.68% and 68.71% for 5-way 1-shot and 5-way 5-shot respectively, which are slightly better than 49.42% and 68.20% in [29].…”

Section: Few-shot Learning Resultsmentioning

confidence: 73%

“…All backbone networks are optimized via SGD by Adam [13] end-to-end on DGX-1. Follow the strategy in [23,26], we pre-train the ConvNet to classify all seen classes and utilizing the optimal weights for model initialization, and we train ResNet from scratch for simplicity. Moreover, We perform TIM strategy in all experiments and set l = 0.5 and h = 1.0 for U(l, h).…”

Section: Experimental Settingsmentioning

confidence: 99%

Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning

Qiao

Shi

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

171

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Few-shot learning, which aims at extracting new concepts rapidly from extremely few examples of novel classes, has been featured into the meta-learning paradigm recently. Yet, the key challenge of how to learn a generalizable classifier with the capability of adapting to specific tasks with severely limited data still remains in this domain. To this end, we propose a Transductive Episodic-wise Adaptive Metric (TEAM) framework for few-shot learning, by integrating the meta-learning paradigm with both deep metric learning and transductive inference. With exploring the pairwise constraints and regularization prior within each task, we explicitly formulate the adaptation procedure into a standard semi-definite programming problem. By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space. Moreover, we further leverage an attention-based bi-directional similarity strategy for extracting the more robust relationship between queries and prototypes. Extensive experiments on three benchmark datasets show that our framework is superior to other existing approaches and achieves the state-ofthe-art performance in the few-shot literature.

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“…It is observed that our CTM method compares favorably against most methods by a large margin, not limited to the metric-based methods but also compared with the optimization-based methods. For example, under the 5way 1-shot setting, the performance is 62.05% vs 59.60% [27], and 64.78% vs 59.91% [23] on the two benchmarks miniImageNet and tieredImageNet, respectively.…”

Section: Comparison Beyond Metric-based Approachesmentioning

confidence: 99%

Finding Task-Relevant Features for Few-Shot Learning by Category Traversal

Eigen

Dodge

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

342

159

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Few-shot learning is an important area of research. Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limited-example training situations are common in practice. Recent effective approaches to few-shot learning employ a metric-learning framework to learn a feature similarity comparison between a query (test) example, and the few support (training) examples. However, these approaches treat each support class independently from one another, never looking at the entire task as a whole. Because of this, they are constrained to use a single set of features for all possible test-time tasks, which hinders the ability to distinguish the most relevant dimensions for the task at hand. In this work, we introduce a Category Traversal Module that can be inserted as a plug-and-play module into most metric-learning based few-shot learners. This component traverses across the entire support set at once, identifying task-relevant features based on both intra-class commonality and inter-class uniqueness in the feature space. Incorporating our module improves performance considerably (5%-10% relative) over baseline systems on both mini-ImageNet and tieredImageNet benchmarks, with overall performance competitive with recent state-of-the-art systems.

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Few-Shot Image Recognition by Predicting Parameters from Activations

Cited by 497 publications

References 18 publications

Boosting Few-Shot Visual Learning With Self-Supervision

Boosting Few-Shot Visual Learning With Self-Supervision

Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning

Finding Task-Relevant Features for Few-Shot Learning by Category Traversal

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