基于语义驱动候选区域生成的全文图像文本行人搜索
Text-based Person Search in Full Images via Semantic-Driven Proposal Generation
AI总结：
本文提出一种端到端框架，利用查询文本语义指导候选区域生成并引入跨尺度视觉-语义嵌入，在CUHK-SYSU和PRW上实现全文图像文本行人搜索的最优性能。
摘要：
在完整场景图像中根据文本描述查询来查找目标行人，在智能视频监控中具有重要的实际应用价值。然而，与真实场景中边界框不可用的情况不同，现有的基于文本的行人检索方法主要关注查询文本描述与裁剪后的行人图像库之间的跨模态匹配。为弥合这一差距，我们研究了全文图像中的文本行人搜索问题，提出了一个新的端到端学习框架，该框架联合优化行人检测、身份识别和视觉-语义特征嵌入任务。为充分利用查询文本，利用语义特征指导区域候选网络（Region Proposal Network）更加关注文本所描述的候选区域。此外，采用了一种跨尺度视觉-语义嵌入机制来提升性能。为验证所提方法，我们基于广泛采用的图像行人搜索数据集CUHK-SYSU和PRW收集并标注了两个大规模基准数据集。在这两个数据集上进行了全面实验，并与基线方法进行比较，我们的方法取得了最先进的性能。
英文摘要：
Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images. To close the gap, we study the problem of text-based person search in full images by proposing a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals. Besides, a cross-scale visual-semantic embedding mechanism is utilized to improve the performance. To validate the proposed method, we collect and annotate two large-scale benchmark datasets based on the widely adopted image-based person search datasets CUHK-SYSU and PRW. Comprehensive experiments are conducted on the two datasets and compared with the baseline methods, our method achieves the state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
