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

Transformers incorporating copula structures have demonstrated remarkable
performance in time series prediction. However, their heavy reliance on
self-attention mechanisms demands substantial computational resources, thus
limiting their practical utility across a wide range of tasks. In this work, we
present a model that combines the perceiver architecture with a copula
structure to enhance time-series forecasting. By leveraging the perceiver as
the encoder, we efficiently transform complex, high-dimensional, multimodal
data into a compact latent space, thereby significantly reducing computational
demands. To further reduce complexity, we introduce midpoint inference and
local attention mechanisms, enabling the model to capture dependencies within
imputed samples effectively. Subsequently, we deploy the copula-based attention
and output variance testing mechanism to capture the joint distribution of
missing data, while simultaneously mitigating error propagation during
prediction. Our experimental results on the unimodal and multimodal benchmarks
showcase a consistent 20\% improvement over the state-of-the-art methods, while
utilizing less than half of available memory resources.

本研究通过将感知机制与 copula 结构相结合，提出了一种模型，以增强时间序列预测。通过将感知机作为编码器，我们将复杂的、高维度、多模态数据有效地转化为紧凑的潜在空间，从而大大降低计算要求。我们进一步引入中点推断和局部注意机制来降低复杂度，使模型能够有效地捕捉缺失样本中的依赖关系。随后，我们部署基于 copula 的注意机制和输出方差测试机制，捕捉缺失数据的联合分布，并在预测过程中减轻错误传播。在单模态和多模态基准测试中，我们的实验结果显示，相对于现有方法，我们的模型在节约了一半内存资源的同时，保持了持续 20% 的改进。