Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognition

Abstract

Zero-shot object recognition or zero-shot learning aims to transfer the object recognition ability among the semantically related categories, such as fine-grained animal or bird species. However, the images of different fine-grained objects tend to merely exhibit subtle differences in appearance, which will severely deteriorate zero-shot object recognition. To reduce the superfluous information in the fine-grained objects, in this paper, we propose to learn the redundancy-free features for generalized zero-shot learning. We achieve our motivation by projecting the original visual features into a new (redundancy-free) feature space and then restricting the statistical dependence between these two feature spaces. Furthermore, we require the projected features to keep and even strengthen the category relationship in the redundancy-free feature space. In this way, we can remove the redundant information from the visual features without losing the discriminative information. We extensively evaluate the performance on four benchmark datasets. The results show that our redundancy-free feature based generalized zero-shot learning (RFF-GZSL) approach can achieve competitive results compared with the state-of-the-arts.

arXiv commentSome researchers and we have found KNN results in 1st version are incorrect, due to a careless mistake in the code. Concretely, the parameters for accuracy function of KNN were organized in the wrong order by mistake. The softmax results are correct. We have removed all KNN results and remove the SOTA claims. According to the Program Chairs' suggestion, we have made errata request to CVF and IEEE

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Zero-Shot Learning Through Cross-Modal Transfer
• Zero-Shot Learning by Convex Combination of Semantic Embeddings
• Generative Moment Matching Networks
• Deep Variational Information Bottleneck
• Generalized Denoising Auto-Encoders as Generative Models
• Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
• Learning Deep Representations of Fine-grained Visual Descriptions
• Leveraging the Invariant Side of Generative Zero-Shot Learning
• Zero-Shot Learning -- The Good, the Bad and the Ugly
• Latent Embeddings for Zero-shot Classification
• Synthesized Classifiers for Zero-Shot Learning