Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by long-range cameras often suffer from atmospheric turbulence, blur, low-resolution, unconstrained poses, and poor illumination. In this paper, we provide a brief survey of recent advances in person recognition at a distance. In particular, we review recent work in multi-spectral face verification, person re-identification, and gait-based analysis techniques. Furthermore, we discuss the merits and drawbacks of existing approaches and identify important, yet under explored challenges for deploying remote person recognition systems in-the-wild.

In recent years, visible-spectrum face verification systems have been shown to match the performance of experienced forensic examiners. However, such systems are ineffective in low-light and nighttime conditions. Thermal face imagery, which captures body heat emissions, effectively augments the visible spectrum, capturing discriminative facial features in scenes with limited illumination. Due to the increased cost and difficulty of obtaining diverse, paired thermal and visible spectrum datasets, not many algorithms and large-scale benchmarks for low-light recognition are available. This paper presents an algorithm that achieves state-of-the-art performance on both the ARL-VTF and TUFTS multi-spectral face datasets. Importantly, we study the impact of face alignment, pixel-level correspondence, and identity classification with label smoothing for multi-spectral face synthesis and verification. We show that our proposed method is widely applicable, robust, and highly effective. In addition, we show that the proposed method significantly outperforms face frontalization methods on profile-to-frontal verification. Finally, we present MILAB-VTF(B), a challenging multi-spectral face dataset that is composed of paired thermal and visible videos. To the best of our knowledge, with face data from 400 subjects, this dataset represents the most extensive collection of indoor and long-range outdoor thermal-visible face imagery. Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.

每年，成千上万的人学习新的视觉分类任务 - 放射科医生学习识别肿瘤，观鸟者学会区分相似的物种，并且人群工人学习如何为自主驾驶等应用提供有价值的数据。随着人类的了解，他们的大脑会更新其提取和关注的视觉功能，最终为他们的最终分类决策提供了信息。在这项工作中，我们提出了一项新的任务，即追踪人类学习者从事挑战性视觉分类任务的不断发展的分类行为。我们提出的模型可以共同提取学习者使用的视觉特征，并预测其使用的分类功能。我们从真正的人类学习者那里收集了三个挑战性的新数据集，以评估不同的视觉知识追踪方法的性能。我们的结果表明，我们的经常性模型能够预测人类学习者对三个具有挑战性的医学形象和物种识别任务的分类行为。

自我监督的学习旨在学习图像特征表示，而无需使用手动注释的标签。它通常用作前体步骤，以获得有用的初始网络权重，这些权重有助于更快的收敛性和下游任务的卓越性能。虽然自我监督允许一个人在没有使用标签的情况下降低监督和无监督学习之间的领域差距，但自我监督目标仍然需要强烈的归纳偏见到下游任务，以便有效转移学习。在这项工作中，我们介绍了我们的材料和纹理的自我监督方法，命名为物质（材料和纹理表示学习），这是由古典材料和纹理方法的启发。材料和质地可以有效地描述任何表面，包括其触觉性质，颜色和镜面。通过延伸，材料和纹理的有效表示可以描述与所述材料和纹理密切相关的其他语义类。物质利用多时间，空间对齐的遥感图像，通过不变的区域来学习与实现材料和纹理表示的一致性的照明和视角的不变性。我们表明，我们的自我监督预训练方法可允许持续高估和微调的设置增加24.22％和6.33％，更快的收敛速度高达76％，更改检测，土地覆盖分类和语义细分任务。