Learning a Deep Embedding Model for Zero-Shot Learning
01 Jul 2017-pp 3010-3019
TL;DR: This paper proposes to use the visual space as the embedding space instead of embedding into a semantic space or an intermediate space, and argues that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective.
Abstract: Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be projected to for nearest neighbour search. Despite the success of deep neural networks that learn an end-to-end model between text and images in other vision problems such as image captioning, very few deep ZSL model exists and they show little advantage over ZSL models that utilise deep feature representations but do not learn an end-to-end embedding. In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space. Instead of embedding into a semantic space or an intermediate space, we propose to use the visual space as the embedding space. This is because that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. This model design also provides a natural mechanism for multiple semantic modalities (e.g.,~attributes and sentence descriptions) to be fused and optimised jointly in an end-to-end manner. Extensive experiments on four benchmarks show that our model significantly outperforms the existing models.
Citations
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18 Jun 2018
TL;DR: 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.
Abstract: We present 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. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on five benchmarks demonstrate that our simple approach provides a unified and effective approach for both of these two tasks.
2,496 citations
Cites background or methods from "Learning a Deep Embedding Model for..."
...This DNN is pre-trained on ILSVRC 2012 1K classification without fine-tuning, as in recent deep ZSL works [25, 30, 45]....
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...We add weight decay (L2 regularisation) in FC1 & 2 as there is a hubness problem [45] in cross-modal mapping for ZSL which can be best solved by mapping the semantic feature vector to the visual feature space with regularisation....
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...The 10 shallow models results are from [42] and the result of the state-of-the-art method DEM [45] is from the authors’ GitHub page1....
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...Motivated by the failure of conventional deep learning methods to work well on one or few examples per class, and inspired by the few- and zero-shot learning ability of humans, there has been a recent resurgence of interest in machine one/few-shot [8, 39, 32, 18, 20, 10, 27, 36, 29] and zero-shot [11, 3, 24, 45, 25, 31] learning....
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...While on AwA1, our method is only outperformed by DEM [45]....
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Posted Content•
TL;DR: A new zero-shot learning dataset is proposed, the Animals with Attributes 2 (AWA2) dataset which is made publicly available both in terms of image features and the images themselves and compares and analyzes a significant number of the state-of-the-art methods in depth.
Abstract: Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.
785 citations
Cites background from "Learning a Deep Embedding Model for..."
...larly, [35] argues that the visual feature space is more discriminative than the semantic space, thus it proposes an end-to-end deep embedding model which maps semantic...
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18 Jun 2018
TL;DR: The authors propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution.
Abstract: Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets - CUB, FLO, SUN, AWA and ImageNet - in both the zero-shot learning and generalized zero-shot learning settings.
783 citations
TL;DR: The Animals with Attributes 2 (AWA2) dataset as mentioned in this paper is a new dataset for zero-shot learning, which is publicly available both in terms of image features and the images themselves.
Abstract: Due to the importance of zero-shot learning, i.e., classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g., pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.
747 citations
18 Jun 2018
TL;DR: In this article, a graph convolutional network (GCN) is used to predict the visual classifiers of unseen categories, which is robust to noise in the learned knowledge graph (KG) given a semantic embedding for each node (representing visual category).
Abstract: We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this paper, we build upon the recently introduced Graph Convolutional Network (GCN) and propose an approach that uses both semantic embeddings and the categorical relationships to predict the classifiers. Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category). After a series of graph convolutions, we predict the visual classifier for each category. During training, the visual classifiers for a few categories are given to learn the GCN parameters. At test time, these filters are used to predict the visual classifiers of unseen categories. We show that our approach is robust to noise in the KG. More importantly, our approach provides significant improvement in performance compared to the current state-of-the-art results (from 2 ~ 3% on some metrics to whopping 20% on a few).
570 citations
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"Learning a Deep Embedding Model for..." refers methods in this paper
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"Learning a Deep Embedding Model for..." refers methods in this paper
...We omit the formulation of the bidirectional LSTM here and refer the readers to [15, 14] for details....
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...We use the BasicLSTMCell in Tensorflow as our RNN cell and employ ReLU as activation function....
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...The word embedding size and the number of LSTM unit are both 512....
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...When the semantic representation was encoded from descriptions for the CUB dataset, a bidirectional LSTM encoding subnet is employed (see Fig....
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...It is worth pointing out that this result was obtained using a word-CNN-RNN neural language model, whilst our model uses a bidirectional LSTM subnet, which is easier to train end-to-end with the rest of the network....
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49,639 citations
"Learning a Deep Embedding Model for..." refers background or methods in this paper
...Extensive experiments carried out on four benchmarks including AwA [22], CUB [45] and large scale ILSVRC 2010 and ILSVRC 2012 [6] show that our model beats all the stateof-the-art models presented to date, often by a large margin....
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...However, most existing recognition models are based on supervised learning and require a large amount (at least 100s) of training samples to be collected and annotated for each object class to capture its intra-class appearance variations [6]....
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