基于多标签协同学习的跨域行人重识别

跨域是行人重识别的重要应用场景，但是源域与目标域行人图像在光照条件、拍摄视角、成像背景与风格等方面的表观特征差异性是导致行人重识别模型泛化能力下降的关键因素。针对该问题，提出了基于多标签协同学习的跨域行人重识别方法。利用语义解析模型构造了基于语义对齐的多标签数据表示，以引导构建更关注行人前景区域的局部特征，达到语义对齐的目的，减少背景对跨域重识别的影响。基于行人图像全局特征和语义对齐后的行人局部特征，利用协同学习平均模型生成行人重识别模型的多标签表示，减少跨域场景下噪声硬标签的干扰。利用协同学习网络框架联合多标签的语义对齐模型，提高行人重识别模型的识别能力。实验结果表明:在Market-1501→ DukeMTMC-reID、DukeMTMC-reID→Market-1501、Market-1501→MSMT17、DukeMTMC-reID→MSMT17跨域行人重识别数据集上，与NRMT方法相比，平均精度均值分别提高了8.3%、8.9%、7.6%、7.9%，多标签协同学习方法具有显著的优越性。 

Multi-label cooperative learning for cross domain person re-identification

Cross-domain was an important application scenario in person re-identification, but the apparent difference of person image in illumination condition, shooting angle, imaging background and style between the source domain and target domain was the most important factor that leads to the decline of the generalization ability of person re-identification model. A cross-domain person re-identification method was proposed based on multi-label cooperative learning to solve the problem. Firstly, the semantic parsing model was used to construct the multi-label data based on semantic alignment, which was able to guide us to construct global features that pay more attention to the person area, achieve the purpose of semantic alignment, and reduce the background influence on cross-domain person re-identification. Furthermore, the collaborative learning average model was used to generate a multi-label representation of the person re-identification model based on global and local features after semantic alignment, reducing the interference of noisy hard labels in the cross-domain scenario. Finally, the semantic alignment model of multi-label based on a collaborative learning network framework was combined to improve the identification ability of re-identification model. The experiment results show that on the Market-1501→DukeMTMC-reID, DukeMTMC-reID→Market-1501, Market-1501→MSMT17, DukeMTMC-reID→MSMT17 cross-domain person re-identification data set, compared with the current state-of-the-artscross-domain person re-identification method NRMT, the mean average precision of this method is increased by 8.3%, 8.9%, 7.6% and 7.9%, respectively. Multi-label cooperative learning method has obvious advantages.