Generalized Zero-Shot Learning (GZSL) aims to recognize objects from both seen and unseen categories by transferring semantic knowledge and merely utilizing seen class data for training. Recent feature generation meth...
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Generalized Zero-Shot Learning (GZSL) aims to recognize objects from both seen and unseen categories by transferring semantic knowledge and merely utilizing seen class data for training. Recent feature generation methods in the 2D image domain have made great progress. However, very little is known about its usefulness in 3D point cloud zero-shot learning. This work aims to facilitate research on 3D point cloud generalized zero-shot learning. Different from previous works, we focus on synthesizing the more high-level discriminative point cloud features. To this end, we design a representation enhancement strategy to generate the features. Specifically, we propose a Contrastive Generative Network with recursive -loop, termed as CGRL, which can be leveraged to enlarge the inter-class distances and narrow the intra-class gaps. By applying the contrastive representations to the generative model in a recursive-loop form, it can provide the self-guidance for the generator recurrently, which can help yield more discriminative features and train a better classifier. To validate the effectiveness of the proposed method, extensive experiments are conducted on three benchmarks, including ModelNet40, McGill, and ScanObjectNN. Experimental evaluations demonstrate the superiority of our approach and it can outperform the state-of-the-arts by a large margin. Code is available at https: //***/photon-git/CGRL
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