Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge

Long Zhao, Xi Peng, Yuxiao Chen, Mubbasir Kapadia, Dimitris N. Metaxas

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

Abstract

Cross-modal knowledge distillation deals with transferring knowledge from a model trained with superior modalities (Teacher) to another model trained with weak modalities (Student). Existing approaches require paired training examples exist in both modalities. However, accessing the data from superior modalities may not always be feasible. For example, in the case of 3D hand pose estimation, depth maps, point clouds, or stereo images usually capture better hand structures than RGB images, but most of them are expensive to be collected. In this paper, we propose a novel scheme to train the Student in a Target dataset where the Teacher is unavailable. Our key idea is to generalize the distilled cross-modal knowledge learned from a Source dataset, which contains paired examples from both modalities, to the Target dataset by modeling knowledge as priors on parameters of the Student. We name our method 'Cross-Modal Knowledge Generalization' and demonstrate that our scheme results in competitive performance for 3D hand pose estimation on standard benchmark datasets.

Original languageEnglish (US)
Article number9156281
Pages (from-to)6527-6536
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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