Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning
arXiv:1805.08113
Abstract
Zero-Shot Learning (ZSL) is achieved via aligning the semantic relationships between the global image feature vector and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-to-end. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.
References in corpus (11)
- Multiple Object Recognition with Visual Attention
- Transductive Multi-view Zero-Shot Learning
- Image Captioning with Semantic Attention
- Zero-Shot Learning via Class-Conditioned Deep Generative Models
- Zero-Shot Learning by Generating Pseudo Feature Representations
- Ridge Regression, Hubness, and Zero-Shot Learning
- Zero-Shot Learning posed as a Missing Data Problem
- Semi-supervised Vocabulary-informed Learning
- Semantically Consistent Regularization for Zero-Shot Recognition
- Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks
- Zero-Shot Learning via Latent Space Encoding