Zero-shot learning, which aims to recognize new categories that are not included in the training set, has gained popularity owing to its potential ability in the real-word applications. Zero-shot learning models rely on learning an embedding space, where both semantic descriptions of classes and visual features of instances can be embedded for nearest neighbor search. Recently, most of the existing works consider the visual space formulated by deep visual features as an ideal choice of the embedding space. However, the discrete distribution of instances in the visual space makes the data structure unremarkable. We argue that optimizing the visual space is crucial as it allows semantic vectors to be embedded into the visual space more effectively. In this work, we propose two strategies to accomplish this purpose. One is the visual prototype based method, which learns a visual prototype for each visual class, so that, in the visual space, a class can be represented by a prototype feature instead of a series of discrete visual features. The other is to optimize the visual feature structure in an intermediate embedding space, and in this method we successfully devise a multilayer perceptron framework based algorithm that is able to learn the common intermediate embedding space and meanwhile to make the visual data structure more distinctive. Through extensive experimental evaluation on four benchmark datasets, we demonstrate that optimizing visual space is beneficial for zero-shot learning. Besides, the proposed prototype based method achieves the new state-of-the-art performance.
Most recently, unseen class information is used to get better performance in the ZSL task @cite_61 @cite_48 @cite_19 @cite_29 @cite_59 @cite_16 @cite_54 . For instance, in the work @cite_61 , unseen information is employed to assist aligning of the visual-semantic structures. As another example, some recent works @cite_19 @cite_29 @cite_59 @cite_16 @cite_54 adopt generative models to enlarge synthesized labeled examples from the unseen classes, and consequently, these examples can be assisted to train a better projection model. Furthermore, a related scenario is the transductive zero-shot learning @cite_23 @cite_13 @cite_44 @cite_27 , which assumes that the unlabeled samples from unseen classes are available during training. However, those works to some extent breach the strict ZSL settings that the testing resources should not be accessed in the training stage. In our work, we make no use of unseen classes information and consider only the seen resources are available at training time.
@cite_61 @cite_48 @cite_29 @cite_54 @cite_44 @cite_19 @cite_27 @cite_59 @cite_23 @cite_16 @cite_13
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Zero-shot learning (ZSL) aims to recognize unseen object classes without any training samples, which can be regarded as a form of transfer learning from seen classes to unseen ones. This is made possible by learning a projection between a feature space and a semantic space (e.g. attribute space). Key to ZSL is thus to learn a projection function that is robust against the often large domain gap between the seen and unseen classes. In this paper, we propose a novel ZSL model termed domain-invariant projection learning (DIPL). Our model has two novel components: (1) A domain-invariant feature self-reconstruction task is introduced to the seen unseen class data, resulting in a simple linear formulation that casts ZSL into a min-min optimization problem. Solving the problem is non-trivial, and a novel iterative algorithm is formulated as the solver, with rigorous theoretic algorithm analysis provided. (2) To further align the two domains via the learned projection, shared semantic structure among seen and unseen classes is explored via forming superclasses in the semantic space. Extensive experiments show that our model outperforms the state-of-the-art alternatives by significant margins. Existing image classification systems often suffer from re-training models for novel unseen classes. Zero-shot learning (ZSL) aims to recognise these unseen classes directly using trained models with a further inference procedure. However, existing approaches highly rely on human-defined class-attribute associations to achieve the inference, which significantly increases the annotation cost. This paper aims to address ZSL on non-attribute tasks, i.e. only training images with labels are used as most of the supervised settings. Our main contributions are: 1) to circumvent expensive attributes, we propose to use semantic similes that directly indicate the unseen-to-seen associations; 2) a novel similarity-based representation is proposed to represent both visual images and semantic similes in a unified embedding space; 3) in order to reduce the annotation cost, we use only a few similes to infer a class-level prototype for each unseen class. On two popular benchmarks, AwA and aPY, extensive experiments manifest that our method can significantly improve the state-of-the-art results using only two similes for each unseen class. Furthermore, we revisit the Caltech 101 dataset without attributes. Our ZSL results can exceed that of previous supervised methods. Sufficient training examples are the fundamental requirement for most of the learning tasks. However, collecting well-labelled training examples is costly. Inspired by Zero-shot Learning (ZSL) that can make use of visual attributes or natural language semantics as an intermediate level clue to associate low-level features with high-level classes, in a novel extension of this idea, we aim to synthesise training data for novel classes using only semantic attributes. Despite the simplicity of this idea, there are several challenges. First, how to prevent the synthesised data from over-fitting to training classes? Second, how to guarantee the synthesised data is discriminative for ZSL tasks? Third, we observe that only a few dimensions of the learnt features gain high variances whereas most of the remaining dimensions are not informative. Thus, the question is how to make the concentrated information diffuse to most of the dimensions of synthesised data. To address the above issues, we propose a novel embedding algorithm named Unseen Visual Data Synthesis (UVDS) that projects semantic features to the high-dimensional visual feature space. Two main techniques are introduced in our proposed algorithm. (1) We introduce a latent embedding space which aims to reconcile the structural difference between the visual and semantic spaces, meanwhile preserve the local structure. (2) We propose a novel Diffusion Regularisation (DR) that explicitly forces the variances to diffuse over most dimensions of the synthesised data. By an orthogonal rotation (more precisely, an orthogonal transformation), DR can remove the redundant correlated attributes and further alleviate the over-fitting problem. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data for zero-shot learning. Extensive experimental results suggest that our proposed approach significantly outperforms the state-of-the-art methods. 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.