In this paper, we present WildlifeDatasets
(this https URL) - an open-source
toolkit intended primarily for ecologists and computer-vision /
machine-learning researchers. The WildlifeDatasets is written in Python, allows
straightforward access to publicly available wildlife datasets, and provides a
wide variety of methods for dataset pre-processing, performance analysis, and
model fine-tuning. We showcase the toolkit in various scenarios and baseline
experiments, including, to the best of our knowledge, the most comprehensive
experimental comparison of datasets and methods for wildlife re-identification,
including both local descriptors and deep learning approaches. Furthermore, we
provide the first-ever foundation model for individual re-identification within
a wide range of species - MegaDescriptor - that provides state-of-the-art
performance on animal re-identification datasets and outperforms other
pre-trained models such as CLIP and DINOv2 by a significant margin. To make the
model available to the general public and to allow easy integration with any
existing wildlife monitoring applications, we provide multiple MegaDescriptor
flavors (i.e., Small, Medium, and Large) through the HuggingFace hub
(this https URL).

该研究论文介绍了 WildlifeDatasets 工具集，一个开源工具集用于生态学家和计算机视觉 / 机器学习研究人员，它使用 Python 编写，提供了简单直接获取公开的野生动物数据集的方法，并提供了各种数据集预处理、性能分析和模型优化方法。我们在不同的场景和基准实验中展示了该工具集，包括了野生动物再识别数据集和方法的最全面比较，包括了局部描述符和深度学习方法。此外，我们提供了首个针对各种动物个体再识别的基础模型 - MegaDescriptor，该模型在动物再识别数据集上表现优异，超过了 CLIP 和 DINOv2 等预训练模型的性能。为了让该模型对大众可用以及方便集成到任何现有的野生动物监测应用中，我们通过 HuggingFace hub 提供了多个 MegaDescriptor 版本（即 Small、Medium 和 Large）。

野生动物数据集：一个用于动物再识别的开源工具包

WildlifeDatasets: An open-source toolkit for animal re-identification

Shadow removal is a computer-vision task that aims to restore the image
content in shadow regions. While almost all recent shadow-removal methods
require shadow-free images for training, in ECCV 2020 Le and Samaras introduces
an innovative approach without this requirement by cropping patches with and
without shadows from shadow images as training samples. However, it is still
laborious and time-consuming to construct a large amount of such unpaired
patches. In this paper, we propose a new G2R-ShadowNet which leverages shadow
generation for weakly-supervised shadow removal by only using a set of shadow
images and their corresponding shadow masks for training. The proposed
G2R-ShadowNet consists of three sub-networks for shadow generation, shadow
removal and refinement, respectively and they are jointly trained in an
end-to-end fashion. In particular, the shadow generation sub-net stylises
non-shadow regions to be shadow ones, leading to paired data for training the
shadow-removal sub-net. Extensive experiments on the ISTD dataset and the Video
Shadow Removal dataset show that the proposed G2R-ShadowNet achieves
competitive performances against the current state of the arts and outperforms
Le and Samaras' patch-based shadow-removal method.

本文提出了一种新的 G2R-ShadowNet 方法，通过使用一组阴影图像及其相应的阴影掩模进行训练，利用阴影生成进行弱监督阴影去除。此方法包含三个子网络，分别为阴影生成、阴影去除和细化，共同以端到端的方式进行训练，通过广泛的实验表明，这种方法在 ISTD 数据集和 Video Shadow Removal 数据集上取得了竞争性结果，并且优于以往方法。

从阴影生成到阴影去除

From Shadow Generation to Shadow Removal

One in twenty-five patients admitted to a hospital will suffer from a
hospital acquired infection. If we can intelligently track healthcare staff,
patients, and visitors, we can better understand the sources of such
infections. We envision a smart hospital capable of increasing operational
efficiency and improving patient care with less spending. In this paper, we
propose a non-intrusive vision-based system for tracking people's activity in
hospitals. We evaluate our method for the problem of measuring hand hygiene
compliance. Empirically, our method outperforms existing solutions such as
proximity-based techniques and covert in-person observational studies. We
present intuitive, qualitative results that analyze human movement patterns and
conduct spatial analytics which convey our method's interpretability. This work
is a step towards a computer-vision based smart hospital and demonstrates
promising results for reducing hospital acquired infections.

本研究提出一种基于计算机视觉的智能医院系统，用于跟踪医务人员、患者和访客的行动，并提高医院的运营效率和患者护理质量，特别是在手部卫生合规性的测量方面优于现有解决方案，为建立计算机视觉智能医院的发展迈出了一步，有望降低医院获得性感染的发生率。