Remote individual animal identification is important for food safety, sport,
and animal conservation. Numerous existing remote individual animal
identification studies have focused on RGB images. In this paper, we tackle
individual penguin identification using hyperspectral (HS) images. To the best
of our knowledge, it is the first work to analyze spectral differences between
penguin individuals using an HS camera. We have constructed a novel penguin HS
image dataset, including 990 hyperspectral images of 27 penguins. We
experimentally demonstrate that the spectral information of HS image pixels can
be used for individual penguin identification. The experimental results show
the effectiveness of using HS images for individual penguin identification. The
dataset and source code are available here:
this https URL

利用高光谱图像进行企鹅的个体识别是第一次分析企鹅个体之间光谱差异的研究工作，实验证明高光谱图像的光谱信息可以用于企鹅的个体识别。

用于个体企鹅识别的高光谱图像数据集

Hyperspectral Image Dataset for Individual Penguin Identification

Graph-level anomaly detection has gained significant attention as it finds
many applications in various domains, such as cancer diagnosis and enzyme
prediction. However, existing methods fail to capture the underlying properties
of graph anomalies, resulting in unexplainable framework design and
unsatisfying performance. In this paper, we take a step back and re-investigate
the spectral differences between anomalous and normal graphs. Our main
observation shows a significant disparity in the accumulated spectral energy
between these two classes. Moreover, we prove that the accumulated spectral
energy of the graph signal can be represented by its Rayleigh Quotient,
indicating that the Rayleigh Quotient is a driving factor behind the anomalous
properties of graphs. Motivated by this, we propose Rayleigh Quotient Graph
Neural Network (RQGNN), the first spectral GNN for graph-level anomaly
detection, providing a new perspective on exploring the inherent spectral
features of anomalous graphs. Specifically, we introduce a novel framework that
consists of two components: the Rayleigh Quotient learning component (RQL) and
Chebyshev Wavelet GNN with RQ-pooling (CWGNN-RQ). RQL explicitly captures the
Rayleigh Quotient of graphs and CWGNN-RQ implicitly explores the spectral space
of graphs. Extensive experiments on 10 real-world datasets show that RQGNN
outperforms the best rival by 6.74% in Macro-F1 score and 1.44% in AUC,
demonstrating the effectiveness of our framework.

我们提出了 Rayleigh Quotient Graph Neural Network (RQGNN)，这是第一个用于图级异常检测的谱 GNN，通过探索异常图的固有谱特征，显著提高了性能。