Semantic Manifold Alignment in Visual Feature Space for Zero-Shot Learning

Zero-Shot Learning (ZSL) is getting more attention for its potential to solve a task without training examples, such as to recognize a category of unseen object in computer vision task. Most existing methods are suffered from hubness problem and semantic gap problem. In this paper, we propose a novel strategy based on Aligning Semantic Manifolds in Feature Space (ASMFS) to boost the performance of ZSL. Considering that the semantic representations must be predicted in the location of their corresponding visual instances, we adjust the predicted unseen semantic representations by the average of their K nearest neighbors (K-NN). The experimental results over two basic ZSL models and four public datasets demonstrate the universal enhancement performance of the proposed strategy. It significantly boosts the existing ZSL approaches with low over cost and outperforms eight state-of-the-art methods.