research-article

3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation

Authors:

Xiangdong Zhou,

Guichun ZhouAuthors Info & Claims

ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval

Pages 508 - 517

https://doi.org/10.1145/3512527.3531359

Published: 27 June 2022 Publication History

Abstract

Image-based object pose estimation sounds amazing because in real applications the shape of object is oftentimes not available or not easy to take like photos. Although it is an advantage to some extent, un-explored shape information in 3D vision learning problem looks like "flaws in jade''. In this paper, we deal with the problem in a reasonable new setting, namely 3D shape is exploited in the training process, and the testing is still purely image-based. We enhance the performance of image-based methods for category-agnostic object pose estimation by exploiting 3D knowledge learned by a multi-modal method. Specifically, we propose a novel contrastive knowledge distillation framework that effectively transfers 3D-augmented image representation from a multi-modal model to an image-based model. We integrate contrastive learning into the two-stage training procedure of knowledge distillation, which formulates an advanced solution to combine these two approaches for cross-modal tasks. We experimentally report state-of-the-art results compared with existing category-agnostic image-based methods by a large margin (up to +5% improvement on ObjectNet3D dataset), demonstrating the effectiveness of our method.

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Cited By

Ma WXu MLi XZhou XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)MultiCAD: Contrastive Representation Learning for Multi-modal 3D Computer-Aided Design ModelsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614982(1766-1776)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614982
Ling ZXing ZZhou XCao MZhou G(2023)PanoSwin: a Pano-style Swin Transformer for Panorama Understanding2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01703(17755-17764)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.01703
Yu ZLi CYang LZheng XBi Mi MLee GYao A(2023)Overcoming the TradeOff between Accuracy and Plausibility in 3D Hand Shape Reconstruction2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00060(544-553)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00060

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cover image ACM Conferences

ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval

June 2022

714 pages

ISBN:9781450392389

DOI:10.1145/3512527

General Chairs:
Vincent Oria
New Jersey Institute of Technology, USA
,
Maria Luisa Sapino
Università degli Studi di Torino, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Brigitte Kerhervé
Université du Québec à Montréal, Canada
,
Program Chairs:
Wen-Huang Cheng
National Yang Ming Chao Tung University, Taiwan
,
Ichiro Ide
Nagoya University, Japan
,
Vivek Singh
Rutgers University, USA

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Published: 27 June 2022

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the National Key Research and Development Program of China

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ICMR '22: International Conference on Multimedia Retrieval

June 27 - 30, 2022

NJ, Newark, USA

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Cited By

Ma WXu MLi XZhou XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)MultiCAD: Contrastive Representation Learning for Multi-modal 3D Computer-Aided Design ModelsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614982(1766-1776)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614982
Ling ZXing ZZhou XCao MZhou G(2023)PanoSwin: a Pano-style Swin Transformer for Panorama Understanding2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01703(17755-17764)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.01703
Yu ZLi CYang LZheng XBi Mi MLee GYao A(2023)Overcoming the TradeOff between Accuracy and Plausibility in 3D Hand Shape Reconstruction2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00060(544-553)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00060

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