论文标题
细粒度重新识别
Fine-Grained Re-Identification
论文作者
论文摘要
对重新识别任务（REID）的研究正在为其许多用例和零射击学习性质掌握计算机愿景的动力。本文提出了一种计算高效的细粒REID模型FGREID，该模型是最早统一图像和视频REID的模型之一，同时保持训练参数的数量最小。 FGREID利用基于视频的预训练和空间特征注意来提高视频和图像REID任务的性能。 FGREID在火星，Ilids-Vid和Prid-2011视频人REID基准上实现了最先进的（SOTA）。消除时间合并产生了一个图像REID模型，该模型超过了Cuhk01和Market1501图像人REID基准的SOTA。 FGREID在车辆REID数据集Veri上也达到了SOTA性能，证明了其概括能力。此外，我们进行一项消融研究，分析影响REID任务上模型性能的关键特征。最后，我们讨论与REID任务有关的道德困境，包括滥用的潜力。这项工作的代码可在https：//github.com/ppriyank/fine-graining-reidentification上公开获得。
Research into the task of re-identification (ReID) is picking up momentum in computer vision for its many use cases and zero-shot learning nature. This paper proposes a computationally efficient fine-grained ReID model, FGReID, which is among the first models to unify image and video ReID while keeping the number of training parameters minimal. FGReID takes advantage of video-based pre-training and spatial feature attention to improve performance on both video and image ReID tasks. FGReID achieves state-of-the-art (SOTA) on MARS, iLIDS-VID, and PRID-2011 video person ReID benchmarks. Eliminating temporal pooling yields an image ReID model that surpasses SOTA on CUHK01 and Market1501 image person ReID benchmarks. The FGReID achieves near SOTA performance on the vehicle ReID dataset VeRi as well, demonstrating its ability to generalize. Additionally we do an ablation study analyzing the key features influencing model performance on ReID tasks. Finally, we discuss the moral dilemmas related to ReID tasks, including the potential for misuse. Code for this work is publicly available at https: //github.com/ppriyank/Fine-grained-ReIdentification.
