In this work, we introduce the BBC-Oxford British Sign Language (BOBSL)
dataset, a large-scale video collection of British Sign Language (BSL). BOBSL
is an extended and publicly released dataset based on the BSL-1K dataset
introduced in previous work. We describe the motivation for the dataset,
together with statistics and available annotations. We conduct experiments to
provide baselines for the tasks of sign recognition, sign language alignment,
and sign language translation. Finally, we describe several strengths and
limitations of the data from the perspectives of machine learning and
linguistics, note sources of bias present in the dataset, and discuss potential
applications of BOBSL in the context of sign language technology. The dataset
is available at this https URL

本文介绍了 BBC-Oxford British Sign Language (BOBSL) 数据集，这是一个大规模的英国手语 (BSL) 视频收集。我们描述了数据集的动机和可用注释的统计数据，通过实验提供了签名识别、手语对齐和手语翻译任务的基线，讨论了数据的优势和局限性以及在手语技术方面修正偏见和潜在应用。

BBC-Oxford 英国手语数据集

BBC-Oxford British Sign Language Dataset

The focus of this work is sign spotting - given a video of an isolated sign,
our task is to identify whether and where it has been signed in a continuous,
co-articulated sign language video. To achieve this sign spotting task, we
train a model using multiple types of available supervision by: (1) watching
existing sparsely labelled footage; (2) reading associated subtitles (readily
available translations of the signed content) which provide additional
weak-supervision; (3) looking up words (for which no co-articulated labelled
examples are available) in visual sign language dictionaries to enable novel
sign spotting. These three tasks are integrated into a unified learning
framework using the principles of Noise Contrastive Estimation and Multiple
Instance Learning. We validate the effectiveness of our approach on low-shot
sign spotting benchmarks. In addition, we contribute a machine-readable British
Sign Language (BSL) dictionary dataset of isolated signs, BSLDict, to
facilitate study of this task. The dataset, models and code are available at
our project page.

本文主要研究了如何识别手语视频中特定手语的位置，通过多种监督方法和学习框架，我们建立了一个机器学习模型并使用低样本数据验证了其有效性，并贡献了一个机器可读的英国手语字典数据集 BSLDict。

观察、阅读和查找：学习如何从多个主管中辨别迹象

Watch, read and lookup: learning to spot signs from multiple supervisors

The goal of this work is to recognise and localise short temporal signals in
image time series, where strong supervision is not available for training.
To this end we propose an image encoding that concisely represents human
motion in a video sequence in a form that is suitable for learning with a
ConvNet. The encoding reduces the pose information from an image to a single
column, dramatically diminishing the input requirements for the network, but
retaining the essential information for recognition.
The encoding is applied to the task of recognizing and localizing signed
gestures in British Sign Language (BSL) videos. We demonstrate that using the
proposed encoding, signs as short as 10 frames duration can be learnt from
clips lasting hundreds of frames using only weak (clip level) supervision and
with considerable label noise.

论文提出了一种图像编码方法，将人体运动在视频序列中的姿态信息简洁地表示为一列，以适合使用 ConvNet 进行学习，应用于英国手语视频中识别和定位手势，仅使用弱（片段级）监督和大量标签噪声训练，可学习出仅有 10 帧持续时间的手势。