Semantic Compression Embedding for Generative Zero-Shot Learning

Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression Embedding Guided Generation (SC-EGG) model, which cascades a semantic compression embedding network (SCEN) and an embedding guided generative network (EGGN). The SCEN extracts a group of attribute-level local features for each sample and further compresses them into the new low-dimension visual feature. Thus, a dense-semantic visual space is obtained. The EGGN learns a mapping from the class-level semantic space to the dense-semantic visual space, thus improving the discriminability of the synthesized dense-semantic unseen visual features. Extensive experiments on three benchmark datasets, i. e. , CUB, SUN and AWA2, demonstrate the significant performance gains of SC-EGG over current state-of-the-art methods and its baselines.

Authors

• Ziming Hong
• Shiming Chen Mohamed bin Zayed University of Artificial Intelligence
• Guo-Sen Xie Nanjing University of Science and Technology
• Wenhan Yang Pengcheng Laboratory, Shenzhen, China
• Jian Zhao Zhongguancun Academy
• Yuanjie Shao
• Qinmu Peng
• Xinge You

Keywords

• Computer Vision: Recognition (object detection, categorization)
• Computer Vision: Transfer, low-shot, semi- and un- supervised learning