Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences

• URL: http://arxiv.org/abs/2004.05749v1
• Date: Mon, 13 Apr 2020 02:57:25 GMT
• Title: Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
• Authors: Longlong Jing, Yucheng Chen, Ling Zhang, Mingyi He, Yingli Tian
• Abstract summary: This paper presents a novel self-supervised learning approach to learn both 2D image features and 3D point cloud features. It exploits cross-modality and cross-view correspondences without using any annotated human labels. The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks.
• Score: 32.01548991331616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D point cloud features, this paper presents a novel and effective self-supervised learning approach to jointly learn both 2D image features and 3D point cloud features by exploiting cross-modality and cross-view correspondences without using any human annotated labels. Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network. Two types of features are fed into a two-layer fully connected neural network to estimate the cross-modality correspondence. The three networks are jointly trained (i.e. cross-modality) by verifying whether two sampled data of different modalities belong to the same object, meanwhile, the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views (i.e. cross-view). The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition, 3D shape recognition, multi-view 2D shape retrieval, 3D shape retrieval, and 3D part-segmentation. Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method.

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