Text-enhanced attribute-based attention for generalized zero-shot fine-grained image classification

We address the generalized zero-shot fine-grained image classification problem, in which classes are visually similar and training images for some classes are not available. We leverage auxiliary information in the form of textual descriptions to facilitate the task. Specifically, we propose a text-enhanced attribute-based attention mechanism to compute features from the most relevant image regions guided from the most relevant attributes. Experiments on two popular datasets of CUB and AWA2 show the effectiveness of the proposed method.