计算机科学
模态(人机交互)
语义学(计算机科学)
人工智能
一致性(知识库)
频道(广播)
自然语言处理
模式识别(心理学)
机器学习
程序设计语言
计算机网络
作者
Yiyuan Zhang,Yuhao Kang,Sanyuan Zhao,Jianbing Shen
标识
DOI:10.1109/tifs.2022.3224853
摘要
Visible-Infrared person Re-Identification (VI-ReID) conducts comprehensive identity analysis on non-overlapping visible and infrared camera sets for intelligent surveillance systems, which face huge instance variations derived from modality discrepancy. Existing methods employ different kinds of network structure to extract modality-invariant features. Differently, we propose a novel framework, named Dual-Semantic Consistency Learning Network (DSCNet), which attributes modality discrepancy to channel-level semantic inconsistency. DSCNet optimizes channel consistency from two aspects, fine-grained inter-channel semantics, and comprehensive inter-modality semantics. Furthermore, we propose Joint Semantics Metric Learning to simultaneously optimize the distribution of the channel-and-modality feature embeddings. It jointly exploits the correlation between channel-specific and modality-specific semantics in a fine-grained manner. We conduct a series of experiments on the SYSU-MM01 and RegDB datasets, which validates that DSCNet delivers superiority compared with current state-of-the-art methods. On the more challenging SYSU-MM01 dataset, our network can achieve 73.89% Rank-1 accuracy and 69.47% mAP value. Our code is available at https://github.com/bitreidgroup/DSCNet.
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