4 research outputs found
Progressive Ensemble Networks for Zero-Shot Recognition
Despite the advancement of supervised image recognition algorithms, their
dependence on the availability of labeled data and the rapid expansion of image
categories raise the significant challenge of zero-shot learning. Zero-shot
learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled
classes to reduce human labeling effort. In this paper, we propose a novel
progressive ensemble network model with multiple projected label embeddings to
address zero-shot image recognition. The ensemble network is built by learning
multiple image classification functions with a shared feature extraction
network but different label embedding representations, which enhance the
diversity of the classifiers and facilitate information transfer to unlabeled
classes. A progressive training framework is then deployed to gradually label
the most confident images in each unlabeled class with predicted pseudo-labels
and update the ensemble network with the training data augmented by the
pseudo-labels. The proposed model performs training on both labeled and
unlabeled data. It can naturally bridge the domain shift problem in visual
appearances and be extended to the generalized zero-shot learning scenario. We
conduct experiments on multiple ZSL datasets and the empirical results
demonstrate the efficacy of the proposed model.Comment: CVPR1
Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels
As an interesting and emerging topic, zero-shot recognition (ZSR) makes it possible to train a recognition model by specifying the category's attributes when there are no labeled exemplars available. The fundamental idea for ZSR is to transfer knowledge from the abundant labeled data in different but related source classes via the class attributes. Conventional ZSR approaches adopt a two-step strategy in test stage, where the samples are projected into the attribute space in the first step, and then the recognition is carried out based on considering the relationship between samples and classes in the attribute space. Due to this intermediate transformation, information loss is unavoidable, thus degrading the performance of the overall system. Rather than following this two-step strategy, in this paper, we propose a novel one-step approach that is able to perform ZSR in the original feature space by using directly trained classifiers. To tackle the problem that no labeled samples of target classes are available, we propose to assign pseudo labels to samples based on the reliability and diversity, which in turn will be used to train the classifiers. Moreover, we adopt a robust SVM that accounts for the unreliability of pseudo labels. Extensive experiments on four datasets demonstrate consistent performance gains of our approach over the state-of-the-art two-step ZSR approaches