  3D cross-modal retrieval is gaining attention in the multimedia community.
Central to this topic is learning a joint embedding space to represent data
from different modalities, such as images, 3D point clouds, and polygon meshes,
to extract modality-invariant and discriminative features. Hence, the
performance of cross-modal retrieval methods heavily depends on the
representational capacity of this embedding space. Existing methods treat all
instances equally, applying the same penalty strength to instances with varying
degrees of difficulty, ignoring the differences between instances. This can
result in ambiguous convergence or local optima, severely compromising the
separability of the feature space. To address this limitation, we propose an
Instance-Variant loss to assign different penalty strengths to different
instances, improving the space separability. Specifically, we assign different
penalty weights to instances positively related to their intra-class distance.
Simultaneously, we reduce the cross-modal discrepancy between features by
learning a shared weight vector for the same class data from different
modalities. By leveraging the Gaussian RBF kernel to evaluate sample
similarity, we further propose an Intra-Class loss function that minimizes the
intra-class distance among same-class instances. Extensive experiments on three
3D cross-modal datasets show that our proposed method surpasses recent
state-of-the-art approaches.
