The lack of automatic tools to identify giant panda makes it hard to keep track of and manage giant pandas in wildlife conservation missions. In this paper, we introduce a new Giant Panda Identification (GPID) task, which aims to identify each individual panda based on an image. Though related to the human re-identification and animal classification problem, GPID is extraordinarily challenging due to subtle visual differences between pandas and cluttered global information. In this paper, we propose a new benchmark dataset iPanda-50 for GPID. The iPanda-50 consists of 6, 874 images from 50 giant panda individuals, and is collected from panda streaming videos. We also introduce a new Feature-Fusion Network with Patch Detector (FFN-PD) for GPID. The proposed FFN-PD exploits the patch detector to detect discriminative local patches without using any part annotations or extra location sub-networks, and builds a hierarchical representation by fusing both global and local features to enhance the inter-layer patch feature interactions. Specifically, an attentional cross-channel pooling is embedded in the proposed FFN-PD to improve the identify-specific patch detectors. Experiments performed on the iPanda-50 datasets demonstrate the proposed FFN-PD significantly outperforms competing methods. Besides, experiments on other fine-grained recognition datasets ( i.e. , CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that the proposed FFN-PD outperforms existing state-of-the-art methods.

中文翻译：

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大熊猫鉴定

缺乏识别大熊猫的自动工具，使得在野生动植物保护任务中难以跟踪和管理大熊猫。在本文中，我们引入了一个新的大熊猫识别（GPID）任务，该任务旨在根据图像识别每个大熊猫。尽管与人类的重新识别和动物分类问题有关，但由于熊猫之间细微的视觉差异和混乱的全球信息，GPID仍然具有极大的挑战性。在本文中，我们为GPID提出了一个新的基准数据集iPanda-50。iPanda-50包含来自50个大熊猫个体的6幅874张图像，是从熊猫流媒体视频中收集的。我们还为GPID引入了带有补丁检测器（FFN-PD）的新功能融合网络。提出的FFN-PD利用补丁检测器来检测区分性局部补丁，而无需使用任何零件批注或额外的位置子网，并通过融合全局和局部特征来增强层间补丁特征交互来构建分层表示。具体而言，注意跨通道池被嵌入在提出的FFN-PD中，以改进特定于标识的补丁检测器。在iPanda-50数据集上进行的实验表明，所提出的FFN-PD明显优于竞争方法。此外，还进行了其他细粒度识别数据集的实验（提出的FFN-PD中嵌入了注意的跨通道合并，以改进特定于标识的补丁检测器。在iPanda-50数据集上进行的实验表明，所提出的FFN-PD明显优于竞争方法。此外，还进行了其他细粒度识别数据集的实验（提出的FFN-PD中嵌入了注意的跨通道合并，以改进特定于标识的补丁检测器。在iPanda-50数据集上进行的实验表明，所提出的FFN-PD明显优于竞争方法。此外，还进行了其他细粒度识别数据集的实验（ IE ，CUB-200-2011，Stanford Cars和FGVC-Aircraft）证明了拟议的FFN-PD优于现有的最新方法。