PaperarXiv:2609.38913
FLOW: Feature-Level Optimal Warping for Generalized Remote Physiological Measurement
arXiv preprint
Licence: CC BY 4.0
TL;DR
FLOW is a domain-generalization add-on for end-to-end remote-PPG networks. It pairs a Temporal Refinement Module with Prototype-based Cross-Temporal Optimal Transport, plus source-consistency and identity-preservation regularizers, and derives a generalization bound under conditional optimal transport. arXiv:2609.38913v1 (30 Sep 2026) carries a CC BY 4.0 licence line and does not give a code URL.
- Authors: Bo Zhao, Junzhe Cao, Dan Guo, Dongmin Huang, Wenjin Wang, Tao Tan, Yue Sun, Zitong Yu
- arXiv:2609.38913v1 (30 Sep 2026); HTML licence line CC BY 4.0
- No code URL in the preprint
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APA
Zhao, B., Cao, J., Guo, D., Huang, D., Wang, W., Tan, T., Sun, Y., & Yu, Z. (2026). FLOW: Feature-Level Optimal Warping for Generalized Remote Physiological Measurement. arXiv preprint. https://arxiv.org/abs/2609.38913
BibTeX
@misc{zhao2026flow,
title = {{FLOW: Feature-Level Optimal Warping for Generalized Remote Physiological Measurement}},
author = {Zhao, Bo and Cao, Junzhe and Guo, Dan and Huang, Dongmin and Wang, Wenjin and Tan, Tao and Sun, Yue and Yu, Zitong},
year = {2026},
howpublished = {arXiv preprint},
eprint = {2609.38913},
archivePrefix = {arXiv}
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