Discriminative Feature-Guided Zero-Shot Learning of 3D Model Classification Algorithm
Zero-shot learning of 3D model classification is a burgeoning topic in the field of 3D vision,aiming to classify untrained 3D models correctly.Aiming at the problem that zero-shot learning of 3D model classifi-cation focus on global information rather than local information,impose mandatory constraints,and ignore the cross-domains semantic-visual differences,resulting in low performance,this paper proposes a discriminative feature-guided zero-short 3D model classification network.Firstly,the local discriminative features,i.e.,real-visual features of the multi-view 3D models,are adaptively captured by the proposed visual feature ex-traction module.Secondly,the semantic representations of the class labels are introduced in the form of word vectors,and their pseudo-visual features are generated by conditional generation adversarial network.Finally,the fine-grained across domains alignment of semantic-visual features is achieved by a novel semantic-content joint loss,which consists of semantic discriminative loss and content-aware loss between real-visual and pseudo-visual features from semantics to contents.The proposed algorithm achieves a Top-1 accuracy rate of 60.9％on the ZS3D dataset,which exceeds with the current best method with an accuracy rate of 2.3 percent-age points.And achieves an accuracy rate of 31.9％,9.9％and 16.6％on the three sub-datasets of Ali,respec-tively,which performs an excellent experimental result and verifies the effectiveness and universality of the proposed algorithm.
3D model classificationzero-shot learningdiscriminative featuresjoint lossfine-grained alignment
NETL
NSTL
万方数据
维普
