Deep learning approaches for animal re-identification have had a major impact
on conservation, significantly reducing the time required for many downstream
tasks, such as well-being monitoring. We propose a method called Recurrence
over Video Frames (RoVF), which uses a recurrent head based on the Perceiver
architecture to iteratively construct an embedding from a video clip. RoVF is
trained using triplet loss based on the co-occurrence of individuals in the
video frames, where the individual IDs are unavailable. We tested this method
and various models based on the DINOv2 transformer architecture on a dataset of
meerkats collected at the Wellington Zoo. Our method achieves a top-1
re-identification accuracy of $49\%$, which is higher than that of the best
DINOv2 model ($42\%$). We found that the model can match observations of
individuals where humans cannot, and our model (RoVF) performs better than the
comparisons with minimal fine-tuning. In future work, we plan to improve these
models by using pre-text tasks, apply them to animal behaviour classification,
and perform a hyperparameter search to optimise the models further.

使用基于 Perceiver 架构的循环头部的方法（RoVF），利用视频剪辑逐次构建嵌入。在 Wellington 动物园收集的浣熊数据集上测试，我们的方法实现了 49% 的首位再识别准确率，高于最佳 DINOv2 模型（42%）。

视频帧重复（RoVF）方法用于非洲狐群体重新识别

Recurrence over Video Frames (RoVF) for the Re-identification of  Meerkats

Deep learning has become the standard methodology to approach computer vision
tasks when large amounts of labeled data are available. One area where
traditional deep learning approaches fail to perform is one-shot learning tasks
where a model must correctly classify a new category after seeing only one
example. One such domain is animal re-identification, an application of
computer vision which can be used globally as a method to automate species
population estimates from camera trap images. Our work demonstrates both the
application of similarity comparison networks to animal re-identification, as
well as the capabilities of deep convolutional neural networks to generalize
across domains. Few studies have considered animal re-identification methods
across species. Here, we compare two similarity comparison methodologies:
Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201,
MobileNetV2, and InceptionV3 architectures considering mean average precision
(mAP)@1 and mAP@5. We consider five data sets corresponding to five different
species: humans, chimpanzees, humpback whales, fruit flies, and Siberian
tigers, each with their own unique set of challenges. We demonstrate that
Triplet Loss outperformed its Siamese counterpart for all species. Without any
species-specific modifications, our results demonstrate that similarity
comparison networks can reach a performance level beyond that of humans for the
task of animal re-identification. The ability for researchers to re-identify an
animal individual upon re-encounter is fundamental for addressing a broad range
of questions in the study of population dynamics and community/behavioural
ecology. Our expectation is that similarity comparison networks are the
beginning of a major trend that could stand to revolutionize animal
re-identification from camera trap data.

展示了将相似度比较网络应用于动物再识别的能力，同时证明了深度卷积神经网络在不同物种之间具有泛化性能，并通过比较两种相似性比较方法：Siamese 和 Triplet-Loss 以及五个数据集的实验结果，表明 Triplet Loss 的表现要优于 Siamese，在考虑均值平均精度（mAP）@ 1 和 mAP@ 5，并且并不需要进行任何针对特定物种的修改。