f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning

When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes. To learn the class conditional distribution of CNN features, these models rely on pairs of image features and class attributes. Hence, they can not make use of the abundance of unlabeled data samples. In this paper, we tackle any-shot learning problems i.e. zero-shot and few-shot, in a unified feature generating framework that operates in both inductive and transductive learning settings. We develop a conditional generative model that combines the strength of VAE and GANs and in addition, via an unconditional discriminator, learns the marginal feature distribution of unlabeled images. We empirically show that our model learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e. inductive and transductive (generalized) zero- and few-shot learning settings. We also demonstrate that our learned features are interpretable: we visualize them by inverting them back to the pixel space and we explain them by generating textual arguments of why they are associated with a certain label.Comment: Accepted at CVPR 201

Learning Cross-domain Semantic-Visual Relation for Transductive Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to learn recognition models for recognizing new classes without labeled data. In this work, we propose a novel approach dubbed Transferrable Semantic-Visual Relation (TSVR) to facilitate the cross-category transfer in transductive ZSL. Our approach draws on an intriguing insight connecting two challenging problems, i.e. domain adaptation and zero-shot learning. Domain adaptation aims to transfer knowledge across two different domains (i.e., source domain and target domain) that share the identical task/label space. For ZSL, the source and target domains have different tasks/label spaces. Hence, ZSL is usually considered as a more difficult transfer setting compared with domain adaptation. Although the existing ZSL approaches use semantic attributes of categories to bridge the source and target domains, their performances are far from satisfactory due to the large domain gap between different categories. In contrast, our method directly transforms ZSL into a domain adaptation task through redrawing ZSL as predicting the similarity/dissimilarity labels for the pairs of semantic attributes and visual features. For this redrawn domain adaptation problem, we propose to use a domain-specific batch normalization component to reduce the domain discrepancy of semantic-visual pairs. Experimental results over diverse ZSL benchmarks clearly demonstrate the superiority of our method

Semantic Embedding Space for Zero-Shot Action Recognition

The number of categories for action recognition is growing rapidly. It is thus becoming increasingly hard to collect sufficient training data to learn conventional models for each category. This issue may be ameliorated by the increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data. Existing ZSL studies focus primarily on image data, and attribute-based semantic representations. In this paper, we address zero-shot recognition in contemporary video action recognition tasks, using semantic word vector space as the common space to embed videos and category labels. This is more challenging because the mapping between the semantic space and space-time features of videos containing complex actions is more complex and harder to learn. We demonstrate that a simple self-training and data augmentation strategy can significantly improve the efficacy of this mapping. Experiments on human action datasets including HMDB51 and UCF101 demonstrate that our approach achieves the state-of-the-art zero-shot action recognition performance.Comment: 5 page