Practical video analytics systems that are deployed in bandwidth constrained
environments like autonomous vehicles perform computer vision tasks such as
face detection and recognition. In an end-to-end face analytics system, inputs
are first compressed using popular video codecs like HEVC and then passed onto
modules that perform face detection, alignment, and recognition sequentially.
Typically, the modules of these systems are evaluated independently using
task-specific imbalanced datasets that can misconstrue performance estimates.
In this paper, we perform a thorough end-to-end evaluation of a face analytics
system using a driving-specific dataset, which enables meaningful
interpretations. We demonstrate how independent task evaluations, dataset
imbalances, and inconsistent annotations can lead to incorrect system
performance estimates. We propose strategies to create balanced evaluation
subsets of our dataset and to make its annotations consistent across multiple
analytics tasks and scenarios. We then evaluate the end-to-end system
performance sequentially to account for task interdependencies. Our experiments
show that our approach provides consistent, accurate, and interpretable
estimates of the system's performance which is critical for real-world
applications.

基于一个驾驶特定数据集进行端到端评估，提出了创造平衡评估子集和使其注释一致的策略，以及顺序评估端到端系统性能以解决任务间的相互依赖性问题。实验证明我们的方法能够提供一致、准确和可解释的系统性能估计，对实际应用至关重要。

面部检测和识别实用视频分析系统的端到端评估

End-to-end Evaluation of Practical Video Analytics Systems for Face  Detection and Recognition

Enhancing word usage is a desired feature for writing assistance. To further
advance research in this area, this paper introduces "Smart Word Suggestions"
(SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end
evaluation and presents a more realistic writing assistance scenario. This task
involves identifying words or phrases that require improvement and providing
substitution suggestions. The benchmark includes human-labeled data for
testing, a large distantly supervised dataset for training, and the framework
for evaluation. The test data includes 1,000 sentences written by English
learners, accompanied by over 16,000 substitution suggestions annotated by 10
native speakers. The training dataset comprises over 3.7 million sentences and
12.7 million suggestions generated through rules. Our experiments with seven
baselines demonstrate that SWS is a challenging task. Based on experimental
analysis, we suggest potential directions for future research on SWS. The
dataset and related codes is available at
this https URL

本篇研究介绍了 “Smart Word Suggestions”（SWS）任务和基准，该任务强调端到端评估，旨在提高写作辅助的效果。SWS 需要识别需要改进的单词或短语，并提供替换建议，实验结果表明 SWS 是一项富有挑战性的任务，为未来的研究提供了方向。

智能写作辅助的词语建议

Smart Word Suggestions for Writing Assistance

In this paper, we present DuTongChuan, a novel context-aware translation
model for simultaneous interpreting. This model allows to constantly read
streaming text from the Automatic Speech Recognition (ASR) model and
simultaneously determine the boundaries of Information Units (IUs) one after
another. The detected IU is then translated into a fluent translation with two
simple yet effective decoding strategies: partial decoding and context-aware
decoding. In practice, by controlling the granularity of IUs and the size of
the context, we can get a good trade-off between latency and translation
quality easily. Elaborate evaluation from human translators reveals that our
system achieves promising translation quality (85.71% for Chinese-English, and
86.36% for English-Chinese), specially in the sense of surprisingly good
discourse coherence. According to an End-to-End (speech-to-speech simultaneous
interpreting) evaluation, this model presents impressive performance in
reducing latency (to less than 3 seconds at most times). Furthermore, we
successfully deploy this model in a variety of Baidu's products which have
hundreds of millions of users, and we release it as a service in our AI
platform.

本文介绍了 DuTongChuan，一种用于同传的新颖上下文感知的翻译模型，它能够不断阅读自动语音识别模型中的流文本并同时确定信息单元的边界，进而使用两种简单而有效的解码策略：部分解码和上下文感知解码将检测到的信息单元翻译为流畅的翻译实现语音到语音的实时翻译，具有优秀的语篇连贯性和较短的延迟，并在百度平台的各种产品中成功运用和释放为服务。