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Deep Cross-Modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-Based 3D Shape Retrieval

  • Jiaxin Chen
  • Yi FangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11217)

Abstract

Due to the large cross-modality discrepancy between 2D sketches and 3D shapes, retrieving 3D shapes by sketches is a significantly challenging task. To address this problem, we propose a novel framework to learn a discriminative deep cross-modality adaptation model in this paper. Specifically, we first separately adopt two metric networks, following two deep convolutional neural networks (CNNs), to learn modality-specific discriminative features based on an importance-aware metric learning method. Subsequently, we explicitly introduce a cross-modality transformation network to compensate for the divergence between two modalities, which can transfer features of 2D sketches to the feature space of 3D shapes. We develop an adversarial learning based method to train the transformation model, by simultaneously enhancing the holistic correlations between data distributions of two modalities, and mitigating the local semantic divergences through minimizing a cross-modality mean discrepancy term. Experimental results on the SHREC 2013 and SHREC 2014 datasets clearly show the superior retrieval performance of our proposed model, compared to the state-of-the-art approaches.

Keywords

Sketch-based 3D shape retrieval Cross-modality transformation Adversarial learning Importance-aware metric learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.NYU Multimedia and Visual Computing Lab, Department of Electrical and Computer EngineeringNew York University Abu DhabiAbu DhabiUAE
  2. 2.Department of Electrical and Computer EngineeringNYU Tandon School of EngineeringNew YorkUSA

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