April 12, 2024, 4:45 a.m. | Zeng YU, Yunxiao Shi

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.07930v1 Announce Type: new
Abstract: Visible-infrared person re-identification (VI-reID) aims at matching cross-modality pedestrian images captured by disjoint visible or infrared cameras. Existing methods alleviate the cross-modality discrepancies via designing different kinds of network architectures. Different from available methods, in this paper, we propose a novel parameter optimizing paradigm, parameter hierarchical optimization (PHO) method, for the task of VI-ReID. It allows part of parameters to be directly optimized without any training, which narrows the search space of parameters and makes …

abstract architectures arxiv cameras cs.ai cs.cv designing hierarchical identification images network novel optimization paper paradigm pedestrian person type via

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Machine Learning Engineer

@ Samsara | Canada - Remote