Focus on Hard Samples: Hierarchical Unbiased Constraints for Cross-Domain 3D Model Retrieval

Abstract

Cross-domain 3D model retrieval facilitates the management of explosively emerging unlabeled 3D models with conveniently available 2D images or RGB-D objects, which has attracted more and more attention. The modality gap between query samples (2D images or RGB-D objects) and 3D models makes the task challenging, and adversarial domain adaptation techniques have achieved success in narrowing such gaps. However, existing methods always pay excessive attention to the samples with high discriminability and transferability, whereas the hard samples with rich information are neglected. Accordingly, we propose hierarchical unbiased constraints to make full use of data at semantic level, sample level and feature level to improve the retrieval performance. At semantic level, we utilize maximum F-norm loss to constrain the semantic prediction results of target domain, which takes advantage of more hard samples to reduce ambiguous predictions and enhance discriminability. At sample level, we propose an adaptive triplet center loss to assign less confident samples with a farther negative class, which reliably compacts samples within the same class and expands the distance across different classes. At feature level, we perform SVD (singular value decomposition) for both source features and target features and suppress the relative value of the largest singular value, so that the information of other eigenvectors can be fully utilized to improve transferability. Experiments on two public datasets validate the superiority of the proposed method, and the ablation study analyzes different roles played by these hierarchical unbiased constraints.