Abstract
Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based on their fine-scale unique morphological characteristics. Current state-of-the-art models for multispecies re- identification are based on deep metric learning representing individual identities by fea- ture vectors in an embedding space, the similarity of which forms the basis for a fast automated identity retrieval. Yet very often, the discriminative information of individual wild animals gets significantly reduced due to the presence of several degradation factors in images, leading to reduced retrieval performance and limiting the downstream eco- logical studies. Here, starting by showing that the extent of this performance reduction greatly varies depending on the animal species (18 wild animal datasets), we introduce an augmented training framework for deep feature extractors, where we apply artificial but diverse degradations in images in the training set. We show that applying this augmented training only to a subset of individuals, leads to an overall increased re-identification performance, under the same type of degradations, even for individuals not seen during training. The introduction of diverse degradations during training leads to a gain of up to 8.5% Rank-1 accuracy to a dataset of real-world degraded animal images, selected using human re-ID expert annotations provided here for the first time. Our work is the first to systematically study image degradation in wildlife re-identification, while introducing all the necessary benchmarks, publicly available code and data, enabling further research on this topic.
Abstract (translated)
野生动物重识别旨在通过匹配查询图像与先前已确定个体的数据库，根据其细微独特的形态特征来辨认单个动物。目前最先进的多物种重识别模型基于深度度量学习，在嵌入空间中用特征向量表示个别身份，这些向量之间的相似性构成了快速自动身份检索的基础。然而，由于图片中存在的多种退化因素（如图像模糊、光照变化等），个体野生动物的鉴别信息往往显著降低，导致检索性能下降，并限制了下游生态学研究。 本文首先展示了这种性能下降的程度在很大程度上取决于动物物种（基于18个野外动物数据集）。随后，我们引入了一个增强训练框架，用于深度特征提取器，在此框架中，我们在训练集中应用多样且人工制造的退化因素。结果显示，即使仅对部分个体进行增强训练，也能显著提升整体重识别性能，并且这种改善在遇到相同类型的降质图像时依然有效，即使是那些未参与训练的个体也不例外。 通过在训练过程中引入多样的退化情况，我们能够在一个实际世界中的动物图像数据集上（该数据集首次提供了基于人类专家重新识别注释筛选），获得高达8.5%的Rank-1准确率提升。我们的研究是首个系统地探讨野生动物重识别中图像降质问题的研究，并且引入了所有必要的基准测试、公开可用的代码和数据，以促进这一领域进一步的研究。
URL
https://arxiv.org/abs/2603.04163
