Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval

Authors

  • Bingrui Wang Tianjin University
  • Yuan Zhou Tianjin University

DOI:

https://doi.org/10.1609/aaai.v37i2.25344

Keywords:

CV: Image and Video Retrieval, CV: Multi-modal Vision, ML: Deep Neural Network Algorithms, ML: Multimodal Learning

Abstract

Zero-shot (ZS) sketch-based three-dimensional (3D) shape retrieval (SBSR) is challenging due to the abstraction of sketches, cross-domain discrepancies between two-dimensional sketches and 3D shapes, and ZS-driven semantic knowledge transference from seen to unseen categories. Extant SBSR datasets suffer from lack of data, and no current SBSR methods consider ZS scenarios. In this paper, we contribute a new Doodle2Object (D2O) dataset consisting of 8,992 3D shapes and over 7M sketches spanning 50 categories. Then, we propose a novel prototype contrastive learning (PCL) method that effectively extracts features from different domains and adapts them to unseen categories. Specifically, our PCL method combines the ideas of contrastive and cluster-based prototype learning, and several randomly selected prototypes of different classes are assigned to each sample. By comparing these prototypes, a given sample can be moved closer to the same semantic class of samples while moving away from negative ones. Extensive experiments on two common SBSR benchmarks and our D2O dataset demonstrate the efficacy of the proposed PCL method for ZS-SBSR. Resource is available at https://github.com/yigohw/doodle2object.

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Published

2023-06-26

How to Cite

Wang, B., & Zhou, Y. (2023). Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval. Proceedings of the AAAI Conference on Artificial Intelligence, 37(2), 2474-2482. https://doi.org/10.1609/aaai.v37i2.25344

Issue

Section

AAAI Technical Track on Computer Vision II