提出新框架提升可见光与红外行人重识别性能
Base-Detail Feature Learning Framework for Visible-Infrared Person Re-Identification
- 分离基础特征与细节特征，分别学习跨模态共享与特有信息
- 在三个数据集上达到领先效果，显著优于现有方法
- 适合夜间或全天候行人识别场景研究者使用
可见光-红外行人重识别（VIReID）为全天候重识别任务提供解决方案，但因可见光（VIS）与红外（IR）模态间差异大，性能仍不理想。现有方法主要挖掘模态共享特征，忽视模态特有细节。为此，本文提出基-细特征学习框架（BDLF），通过无损细节特征提取模块和互补基础嵌入生成机制，分别学习基础与细节特征，并引入新型相关性约束方法，确保所获特征在VIS与IR间同时丰富基础与细节知识。在SYSU-MM01、RegDB和LLCM三个数据集上的全面实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Visible-infrared person re-identification (VIReID) provides a solution for ReID tasks in 24-hour scenarios; however, significant challenges persist in achieving satisfactory performance due to the substantial discrepancies between visible (VIS) and infrared (IR) modalities. Existing methods inadequately leverage information from different modalities, primarily focusing on digging distinguishing features from modality-shared information while neglecting modality-specific details. To fully utilize differentiated minutiae, we propose a Base-Detail Feature Learning Framework (BDLF) that enhances the learning of both base and detail knowledge, thereby capitalizing on both modality-shared and modality-specific information. Specifically, the proposed BDLF mines detail and base features through a lossless detail feature extraction module and a complementary base embedding generation mechanism, respectively, supported by a novel correlation restriction method that ensures the features gained by BDLF enrich both detail and base knowledge across VIS and IR features. Comprehensive experiments conducted on the SYSU-MM01, RegDB, and LLCM datasets validate the effectiveness of BDLF.
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