通过重建数据分布，提升遮挡下行人重识别的准确性。
DDRN:a Data Distribution Reconstruction Network for Occluded Person Re-Identification
- 用生成模型重构数据分布，过滤无关干扰信息。
- 在Occluded-Duke上达62.4% mAP，超越最新方法。
- 适合处理遮挡严重场景的行人识别任务。
在遮挡行人重识别中，严重遮挡导致大量无关信息干扰个体准确识别，主要来自背景和遮挡物干扰，影响最终检索效果。传统判别模型依赖图像具体内容与位置，易在遮挡情况下误判。为此，我们提出数据分布重建网络（DDRN），一种利用数据分布的生成模型，可过滤无关细节，增强特征感知能力并减少干扰。此外，严重遮挡使特征空间复杂化，我们设计多中心策略，通过提出的分层子中心Arcface（HS-Arcface）损失函数，更好逼近复杂特征空间。在Occluded-Duke数据集上，mAP达62.4%（+1.1%），rank-1准确率为71.3%（+0.6%），显著超越最新SOTA方法（FRT）。
原文摘要 · Abstract (English)
In occluded person re-identification(ReID), severe occlusions lead to a significant amount of irrelevant information that hinders the accurate identification of individuals. These irrelevant cues primarily stem from background interference and occluding interference, adversely affecting the final retrieval results. Traditional discriminative models, which rely on the specific content and positions of the images, often misclassify in cases of occlusion. To address these limitations, we propose the Data Distribution Reconstruction Network (DDRN), a generative model that leverages data distribution to filter out irrelevant details, enhancing overall feature perception ability and reducing irrelevant feature interference. Additionally, severe occlusions lead to the complexity of the feature space. To effectively handle this, we design a multi-center approach through the proposed Hierarchical SubcenterArcface (HS-Arcface) loss function, which can better approximate complex feature spaces. On the Occluded-Duke dataset, we achieved a mAP of 62.4\% (+1.1\%) and a rank-1 accuracy of 71.3\% (+0.6\%), surpassing the latest state-of-the-art methods(FRT) significantly.
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