Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global priors. Local priors driven by data from seen classes, i.e. classes that are available at training time, become instrumental in recovering unseen classes, i.e. classes that are missing at training time, in a generalized ZSL setting. Hyperparameters of the Bayesian model offer a convenient way to optimize the trade-off between seen and unseen class accuracy in addition to guiding other aspects of model fitting. We conduct experiments on seven benchmark datasets including the large scale ImageNet and show that our model improves the current state of the art in the challenging generalized ZSL setting.
Generative models for zero-shot learning. Although most of the early work focused on discriminative models there are a few studies that use generative models to tackle ZSL @cite_22 @cite_0 . The study in @cite_22 uses Normal distributions to model both image features and semantic vectors and learns a multimodal mapping between two spaces. This mapping is optimized by minimizing a similarity based cross domain loss function. In a similar fashion the study in @cite_0 utilizes a regression model to optimize a mapping between class attributes and parameters of class conditional distributions. A comprehensive review of these techniques and their performance on several benchmark data sets can be found in @cite_6 .
@cite_0 @cite_22 @cite_6
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With the sustaining bloom of multimedia data, Zero-shot Learning (ZSL) techniques have attracted much attention in recent years for its ability to train learning models that can handle “unseen” categories. Existing ZSL algorithms mainly take advantages of attribute-based semantic space and only focus on static image data. Besides, most ZSL studies merely consider the semantic embedded labels and fail to address domain shift problem. In this paper, we purpose a deep two-output model for video ZSL and action recognition tasks by computing both spatial and temporal features from video contents through distinct Convolutional Neural Networks (CNNs) and training a Multi-layer Perceptron (MLP) upon extracted features to map videos to semantic embedding word vectors. Moreover, we introduce a domain adaptation strategy named “ConSSEV” — by combining outputs from two distinct output layers of our MLP to improve the results of zero-shot learning. Our experiments on UCF101 dataset demonstrate the purposed model has more advantages associated with more complex video embedding schemes, and outperforms the state-of-the-art zero-shot learning techniques. Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most existing ZSL methods exploit deterministic transfer learning via an in-between semantic embedding space. In this paper, we try to attack this problem from a generative probabilistic modelling perspective. We assume for any category, the observed representation, e.g. images or texts, is developed from a unique prototype in a latent space, in which the semantic relationship among prototypes is encoded via linear reconstruction. Taking advantage of this assumption, virtual instances of unseen classes can be generated from the corresponding prototype, giving rise to a novel ZSL model which can alleviate the domain shift problem existing in the way of direct transfer learning. Extensive experiments on three benchmark datasets show our proposed model can achieve state-of-the-art results. Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing the conventional model training requirement of annotated examples for every category. This is achieved by establishing a mapping connecting low-level features and a semantic description of the label space, referred as visual-semantic mapping, on auxiliary data. Re-using the learned mapping to project target videos into an embedding space thus allows novel-classes to be recognised by nearest neighbour inference. However, existing ZSL methods suffer from auxiliary-target domain shift intrinsically induced by assuming the same mapping for the disjoint auxiliary and target classes. This compromises the generalisation accuracy of ZSL recognition on the target data. In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes. Specifically: (1) We introduce a multi-task visual-semantic mapping to improve generalisation by constraining the semantic mapping parameters to lie on a low-dimensional manifold, (2) We explore prioritised data augmentation by expanding the pool of auxiliary data with additional instances weighted by relevance to the target domain. The proposed new model is applied to the challenging zero-shot action recognition problem to demonstrate its advantages over existing ZSL models. Zero-shot learning (ZSL) aims to transfer knowledge from observed classes to the unseen classes, based on the assumption that both the seen and unseen classes share a common semantic space, among which attributes enjoy a great popularity. However, few works study whether the human-designed semantic attributes are discriminative enough to recognize different classes. Moreover, attributes are often correlated with each other, which makes it less desirable to learn each attribute independently. In this paper, we propose to learn a latent attribute space, which is not only discriminative but also semantic-preserving, to perform the ZSL task. Specifically, a dictionary learning framework is exploited to connect the latent attribute space with attribute space and similarity space. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.