Abstract
Zero-shot learning for visual recognition has received
much interest in the most recent years. However, the semantic gap across visual features and their underlying semantics is still the biggest obstacle in zero-shot learning.
To fight off this hurdle, we propose an effective Low-rank
Embedded Semantic Dictionary learning (LESD) through
ensemble strategy. Specifically, we formulate a novel framework to jointly seek a low-rank embedding and semantic dictionary to link visual features with their semantic representations, which manages to capture shared features across different observed classes. Moreover, ensemble strategy is adopted to learn multiple semantic dictionaries to constitute the latent basis for the unseen classes.
Consequently, our model could extract a variety of visual
characteristics within objects, which can be well generalized to unknown categories. Extensive experiments on several zero-shot benchmarks verify that the proposed model
can outperform the state-of-the-art approaches.