
行人重识别ReID论文笔记
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记录ReID论文
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基于视频的行人重识别结果可视化
基于视频的行人重识别结果可视化基于视频的行人重识别结果可视化数据集读取test结果保存结果读取gallery排序结果可视化结果结束语基于视频的行人重识别结果可视化在网上搜集了很多帖子,想要白嫖可视化代码。但是发现并没有人分享,是视频重识别太小众了还单帧重识别太受宠了!!!!为此在郑哲东的基础上改出了基于视频的行人重识别结果可视化。郑哲东大佬的代码先放在这里:https://github.com/layumi/Person_reID_baseline_pytorch单帧可视化的代码就是里面的demo原创 2022-01-04 16:06:36 · 4318 阅读 · 10 评论 -
行人重识别阅读笔记之Cross-modality Person re-identification with Shared-Specific Feature Transfer
行人重识别阅读笔记之Cross-modality Person re-identification with Shared-Specific Feature Transfer摘要介绍模型Two-stream feature extractorShared-Specific Transfer NetworkShared and specific complementary learningOptimization论文地址:Cross-modality Person re-identification wit原创 2020-12-08 18:16:14 · 944 阅读 · 0 评论 -
行人重识别阅读笔记之Multi-Granularity Reference-Aided Attentive Feature Aggregation
行人重识别阅读笔记之Multi-Granularity Reference-Aided Attentive Feature Aggregation摘要介绍模型概述Reference-aided Attentive Feature AggregationMulti-Granularity AttentionLoss Design论文地址:https://arxiv.org/pdf/2003.12224.pdf摘要帧间存在冗余、新显示的外观、遮挡和运动模糊。提出多粒度参考辅助注意力特征聚合Multi-G原创 2020-12-08 17:32:33 · 718 阅读 · 0 评论 -
行人重识别阅读笔记之Spatial-Temporal Graph Convolutional Network for Video-based Person Re-identification
行人重识别阅读笔记之Spatial-Temporal Graph Convolutional Network for Video-based Person Re-identification摘要介绍模型PreliminaryPatch Graph ConstructionTemporal GCN ModuleStructural GCN ModuleModel and Loss Functions论文连接:Spatial-Temporal Graph Convolutional Network for V原创 2020-12-08 17:13:51 · 2232 阅读 · 3 评论 -
行人重识别阅读笔记之Salience-Guided Cascaded Suppression Network for Person Re-identification
行人重识别阅读笔记之Salience-Guided Cascaded Suppression Network for Person Re-identification介绍网络结构残差双重注意力模型Residual Dual Attention ModuleChannel-wise AttentionResidual Spatial Attention非局部多阶段特征融合Non-local Multistage Feature Fusion显著特征提取装置Salient Feature Extraction原创 2020-11-13 20:41:52 · 1204 阅读 · 0 评论 -
行人重识别阅读笔记之Unity Style Transfer for Person Re-Identification
行人重识别阅读笔记之Unity Style Transfer for Person Re-Identification摘要主要问题CamStyle的不足UnityGAN论文贡献模型UnityGANUnityStyleDeep Re-ID ModelPipelineTrainingTesting总结摘要风格变换一直是ReID的一个主要挑战,其目的是在不同的摄像机下匹配相同的行人。现有的研究试图用相机不变描述子空间学习来解决这个问题。当不同相机拍摄的图像差异较大时,会产生更多的图像伪影。为了解决这个问,作原创 2020-11-07 17:37:40 · 1347 阅读 · 5 评论 -
行人重识别之RGB-Infrared Cross-Modality Person Re-identification via Joint Pixel and Feature Alignment
行人重识别之RGB-Infrared Cross-Modality Person Re-identification via Joint Pixel and Feature Alignment简介主要问题模型结构1、像素对齐模块 Pixel Alignment Module2、特征对齐模块 Feature Alignment Module3、联合判别模块 Joint Discriminator Module简介paper:https://arxiv.org/abs/1910.05839本文作者关注到原创 2020-11-01 23:12:09 · 691 阅读 · 2 评论