Human parsing has been extensively studied recently (Yamaguchi et al. 2012; Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks of high-quality samples to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient crossdomain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. Besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem. Abstract—This paper presents a robust Joint Discriminative appearance model based Tracking method using online random forests and mid-level feature (superpixels). To achieve superpixel- wise discriminative ability, we propose a joint appearance model that consists of two random forest based models, i.e., the Background-Target discriminative Model (BTM) and Distractor- Target discriminative Model (DTM). More specifically, the BTM effectively learns discriminative information between the target object and background. In contrast, the DTM is used to suppress distracting superpixels which significantly improves the tracker’s robustness and alleviates the drifting problem. A novel online random forest regression algorithm is proposed to build the two models. The BTM and DTM are linearly combined into a joint model to compute a confidence map. Tracking results are estimated using the confidence map, where the position and scale of the target are estimated orderly. Furthermore, we design a model updating strategy to adapt the appearance changes over time by discarding degraded trees of the BTM and DTM and initializing new trees as replacements. We test the proposed tracking method on two large tracking benchmarks, the CVPR2013 tracking benchmark and VOT2014 tracking challenge. Experimental results show that the tracker runs at real-time speed and achieves favorable tracking performance compared with the state-of-the-art methods. The results also sug- gest that the DTM improves tracking performance significantly and plays an important role in robust tracking.

这个部分包含了19篇cross-module ReID 和1篇人脸识别的paper及阅读笔记，从2017-2020目前能找到的所有的跨模态RdID 文章，方便大家使用

跨镜追踪（Person Re-Identification，简称 ReID）技术是现在计算机视觉研究的热门方向，主要解决跨摄像头跨场景下行人的识别与检索。该技术能够根据行人的穿着、体态、发型等信息认知行人，与人脸识别结合能够适用于更多新的应用场景，将人工智能的认知水平提高到一个新阶段。

由于作者代码更新太快，在这里存一下2020年6月20的版本。https://github.com/JDAI-CV/fast-reid

ckpt-person-reid-pytorch-deep-sort_20211201.rar

PLabel 半自动标注系统是基于BS架构，纯Web页面操作，由鹏城实验室叶齐祥、曾炜、田永鸿教授团队自主研发，由工程师邹安平维护，集成视频抽帧，目标检测、视频跟踪、ReID分类、人脸检测等算法，实现了对图像，视频的自动标注，并可以对自动算法的结果进行人工标注，最终得到标注结果，同时也可以对视频、图片、医疗（包括dicom文件及病理图像）相关的数据进行人工标注，标注结果支持COCO及VOC格式。支持多人协同标注。 半自动标注系统主要功能有：用户管理，数据集管理，自动标注，人工标注，ReID标注，车流统计，视频标注，医疗CT标注，超大图像标注，模型管理与重训，报表管理。数据标注过程一个非常重要的因素是数据安全，在标注使用中防止数据泄露，采用基于web标注工具是有效避免数据泄露的措施之一。 半自动标注系统以保证性能的情况下最小化人工标注代价为目标，不断提升自动标注效率，减少人工标注和人工参与过

它是从两个对齐的摄像头（一个可见，一个远红外）收集的。总共有412人。每个人有10个可见光图像和10个远红外图像。

Person Re-identification:Past, Present and Future.pdf

郑哲东 Deep-ReID：行人重识别的深度学习方法。 Person re-identification Background Learn pedestrian representations from

2018年CVPR所有ReID相关领域论文的详细分析解读汇总，还包括若干篇论文的全文翻译。