Class Semantic Attribute Perception Guided Zero-Shot Learning

Deep learning has achieved remarkable success in supervised image classification tasks, which relies on a large number of labeled samples for each class. Recently, zero-shot learning has garnered significant attention, which aims to recognize unseen classes using only training samples from seen classes. To bridge the gap between images and classes, class semantic attributes are introduced, making the alignment between image and class semantic attributes critical to zero-shot learning. However, existing methods often struggle to accurately focus on the image regions corresponding to individual class semantic attributes and tend to overlook the relations between different regions of an image, leading to poor alignment. To address these challenges, we propose a class semantic attribute perception guided zero-shot learning method. Specifically, we achieve coarse-grained perception of class semantic attributes across the entire image through contrastive semantic learning. Additionally, we attain fine-grained perception of individual class semantic attributes within image regions via region partitioning-based attribute alignment, which fully considers the relations between different regions of an image. By integrating these two processes into a unified network, we achieve multi-grained class semantic attribute perception, thereby enhancing the alignment between images and class semantic attributes. We validate the effectiveness of the proposed method on zero-shot learning benchmark data sets.