In recent times, sketch-based 3D shape retrieval has emerged as a pivotal theme and garnered considerable attention within the area of cross-modal retrieval. As a prevalent 3D shape modality, the exponential growth in the quantity of 3D point clouds has boosted a substantial increase in the demand for 3D point cloud retrieval. Simultaneously, due to the absence of prior knowledge about unseen classes, transferring models learned from seen classes to tackle the data from unseen classes effectively remains a significant hurdle in cross-modal retrieval. In light of this, a novel meta-learned zero-shot sketch-based point cloud retrieval (MetaZS-SBPR) network is proposed in this article for exploring cross-modal consistent feature representation from 2D sketches and 3D point clouds, while effectively transferring the knowledge from seen classes to unseen classes. Specifically, a perspective-predicted point cloud feature learning module is presented to capture discriminative features of point clouds from predicted perspectives, thereby mitigating the modal differences across point clouds and sketches. Additionally, a meta zero-shot retrieval strategy is introduced to investigate the knowledge transfer from seen classes to unseen classes harnessing meta-learning, thereby enabling the efficient retrieval of the point clouds from unseen classes. Experimental evaluations conducted on the ZS-SBPR benchmark dataset affirm the effectiveness of the proposed MetaZS-SBPR.

Face sketch recognition focuses on retrieving face photos that have the same identity as query face sketches, and plays a vital role in the field of information forensics and security. Owing to the large cross-modal differences between face sketches and photos, extracting and aligning cross-modal features is still considered a challenging task in the face sketch recognition community. This paper presents a novel cross-modal aligned identity-discriminative feature learning network (CAIFL-Net) for face sketch recognition. Specifically, in this paper, an identity-discriminative feature preservation module is designed to capture the identity-discriminative features of face sketches and photos by eliminating features that are weakly related to recognition. In addition, a sketch-photo cross-reconstructed feature alignment module is proposed to obtain cross-modal aligned features for effective recognition by reconstructing and embedding global features of one modality into another. Extensive experiments on the Uom-SGFS and CUFSF datasets demonstrate the effectiveness of the proposed CAIFL-Net.