Drastic variations in illumination across surveillance cameras make the
person re-identification problem extremely challenging. Current large scale
re-identification datasets have a significant number of training subjects, but
lack diversity in lighting conditions. As a result, a trained model requires
fine-tuning to become effective under an unseen illumination condition. To
alleviate this problem, we introduce a new synthetic dataset that contains
hundreds of illumination conditions. Specifically, we use 100 virtual humans
illuminated with multiple HDR environment maps which accurately model realistic
indoor and outdoor lighting. To achieve better accuracy in unseen illumination
conditions we propose a novel domain adaptation technique that takes advantage
of our synthetic data and performs fine-tuning in a completely unsupervised
way. Our approach yields significantly higher accuracy than semi-supervised and
unsupervised state-of-the-art methods, and is very competitive with supervised
techniques.

本研究通过构建包含数百种照明条件的新的合成数据集以及采用一种新颖的领域自适应技术来解决监控摄像头之间照明差异引起的重识别问题，取得了与监督技术相当的效果。

通过综合实现域自适应以进行无监督的人员重新识别

Domain Adaptation through Synthesis for Unsupervised Person  Re-identification

Most existing datasets for speaker identification contain samples obtained
under quite constrained conditions, and are usually hand-annotated, hence
limited in size. The goal of this paper is to generate a large scale
text-independent speaker identification dataset collected 'in the wild'. We
make two contributions. First, we propose a fully automated pipeline based on
computer vision techniques to create the dataset from open-source media. Our
pipeline involves obtaining videos from YouTube; performing active speaker
verification using a two-stream synchronization Convolutional Neural Network
(CNN), and confirming the identity of the speaker using CNN based facial
recognition. We use this pipeline to curate VoxCeleb which contains hundreds of
thousands of 'real world' utterances for over 1,000 celebrities. Our second
contribution is to apply and compare various state of the art speaker
identification techniques on our dataset to establish baseline performance. We
show that a CNN based architecture obtains the best performance for both
identification and verification.

本文介绍了一种基于计算机视觉技术的全自动管道，用于从开源媒体中创建一个大规模的必须文本无关语音识别数据集，并在其上应用和比较不同的演讲者识别技术以建立性能基线。

VoxCeleb：一个大规模的说话人识别数据集

VoxCeleb: a large-scale speaker identification dataset

Pushing by big data and deep convolutional neural network (CNN), the
performance of face recognition is becoming comparable to human. Using private
large scale training datasets, several groups achieve very high performance on
LFW, i.e., 97% to 99%. While there are many open source implementations of CNN,
none of large scale face dataset is publicly available. The current situation
in the field of face recognition is that data is more important than algorithm.
To solve this problem, this paper proposes a semi-automatical way to collect
face images from Internet and builds a large scale dataset containing about
10,000 subjects and 500,000 images, called CASIAWebFace. Based on the database,
we use a 11-layer CNN to learn discriminative representation and obtain
state-of-theart accuracy on LFW and YTF. The publication of CASIAWebFace will
attract more research groups entering this field and accelerate the development
of face recognition in the wild.

该研究提出了一种半自动方式从互联网收集人脸图像并构建了一个包含大约 10000 个主体和 500000 个图像的大规模数据集，命名为 CASIAWebFace，使用该数据库，我们使用一个 11 层 CNN 学习区分性表示并获得在 LFW 和 YTF 上的最新准确性。