Label Correction For Sketch-Based 3d Shape Retrieval

Abstract

Sketch-based 3D shape retrieval has become an intense topic in the multimedia society. Recent progress in this field can be mainly attributed to well-designed loss functions. However, two essential aspects remain to be considered by previous works: Firstly, the inherent abstract, sparse, and diverse nature of the sketch leads to inevitable label noise, which significantly impairs model efficacy. Secondly, a deficient focus on hard sample mining can lead to model performance degradation. To address these issues, we propose a label self-correction method. Starting from the margin-based loss function, we relate the ground truth class center of the sample to its nearest negative class center. By leveraging the decision boundary, this module bolsters the learning capacity within similar classes, ensuring the separability of the feature space. Extensive experiments on two benchmarks shows that our method surpasses previous state-of-the-art methods.