论文标题
与图像检索和重新识别的关键点对齐的嵌入
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification
论文作者
论文摘要
学习对物体姿势不变的学习嵌入在视觉图像检索和重新识别中至关重要。由于形状可变形和不同的相机观点，现有的人，车辆或动物重新识别任务的方法遭受了较高的级别差异。为了克服这一限制，我们建议将嵌入的图像与预定义的关键点对齐。提出的关键点对齐嵌入模型（KAE-NET）通过多任务学习来学习零件级的功能，该功能以关键点位置为指导。更具体地说，KAE-NET通过学习该关键点的热图重建的辅助任务，从特定关键点激活的特征图中提取通道。 KAE-NET紧凑，通用和概念上的简单。它在CUB-200-2011，CARS196和VERI-776的基准数据集上实现了最先进的性能，用于检索和重新识别任务。
Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification. The existing approaches for person, vehicle, or animal re-identification tasks suffer from high intra-class variance due to deformable shapes and different camera viewpoints. To overcome this limitation, we propose to align the image embedding with a predefined order of the keypoints. The proposed keypoint aligned embeddings model (KAE-Net) learns part-level features via multi-task learning which is guided by keypoint locations. More specifically, KAE-Net extracts channels from a feature map activated by a specific keypoint through learning the auxiliary task of heatmap reconstruction for this keypoint. The KAE-Net is compact, generic and conceptually simple. It achieves state of the art performance on the benchmark datasets of CUB-200-2011, Cars196 and VeRi-776 for retrieval and re-identification tasks.
