The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations.

F .1 Why is There A Sample Imbalance Between Underwater and Open-air Objects?

The current state of re-identification (ReID) models poses limitations to their applicability in the open world, as they are primarily designed and trained for specific categories like person or vehicle. In light of the importance of ReID technology for tracking wildlife populations and migration patterns, we propose a new task called ``Re-identify Any Animal in the Wild'' (ReID-AW). This task aims to develop a ReID model capable of handling any unseen wildlife category it encounters. To address this challenge, we have created a comprehensive dataset called Wildlife-71, which includes ReID data from 71 different wildlife categories. This dataset is the first of its kind to encompass multiple object categories in the realm of ReID.

This work was done under the supervison of Dr. Quanshi Zhang. Please see Section G for details of the dataset, and the selection of sample features and regional features. Eq. (3) of the paper, we assume that all features This section provides detailed derivations on the learning of the mixture model in Section 3.2 of the Therefore, the optimization can be derived as follows. This section provides more discussions on the quantification of knowledge points. According to Section 3.4 of the paper, a regional feature is a knowledge point if it is discriminative enough for classification, i.e.