Global Semantic Consistency for Zero-Shot Learning
This addresses the challenge of classifying unseen categories without training samples, which is crucial for real-world applications where data coverage is incomplete, though it appears incremental as it builds on existing ZSL methods.
The paper tackles the problem of zero-shot learning (ZSL) and generalized zero-shot learning (GZSL) in image recognition by proposing the Global Semantic Consistency Network (GSC-Net), which leverages semantic information from both seen and unseen classes and includes a soft label embedding loss and parametric novelty detection, achieving state-of-the-art performance on three visual attribute datasets.
In image recognition, there are many cases where training samples cannot cover all target classes. Zero-shot learning (ZSL) utilizes the class semantic information to classify samples of the unseen categories that have no corresponding samples contained in the training set. In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning. We also adopt a soft label embedding loss to further exploit the semantic relationships among classes. To adapt GSC-Net to a more practical setting, Generalized Zero-shot Learning (GZSL), we introduce a parametric novelty detection mechanism. Our approach achieves the state-of-the-art performance on both ZSL and GZSL tasks over three visual attribute datasets, which validates the effectiveness and advantage of the proposed framework.