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爱可可AI论文推介(10月17日)( 二 )


爱可可AI论文推介(10月17日)文章插图
爱可可AI论文推介(10月17日)文章插图
爱可可AI论文推介(10月17日)文章插图
爱可可AI论文推介(10月17日)文章插图
3、[CL]*Learning Adaptive Language Interfaces through Decomposition
S Karamcheti, D Sadigh, P Liang
[Stanford University]
基于抽象和分解的交互式自然语言界面 ， 其目标是有效且可靠地从真实的人类用户交互中学习 ， 以完成模拟机器人设置中的任务 。 引入神经语义解析系统 ， 可通过分解来学习新的高级抽象：用户通过交互将描述新行为的高级指令分解成系统可以理解的低级步骤来教授系统 。
Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand. Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples. Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods. Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught. At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge. We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.
爱可可AI论文推介(10月17日)文章插图
爱可可AI论文推介(10月17日)文章插图
爱可可AI论文推介(10月17日)文章插图
4、[CV] MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking
P Dendorfer, A O?ep, A Milan, K Schindler, D Cremers, I Reid, S Roth, L Leal-Taixé
[Technical University Munich & Amazon Research & ETH Zurich & The University of Adelaide]
单摄像头多目标跟踪基准MOTChallenge ， 包含约35,000帧的连续镜头和近700,000个已进行标注的行人 ， 相比之前版本 ， 不仅显著增加了标记框数量 ， 还为行人以外的多种对象类提供了标签 ， 以及每个兴趣对象的可见度级别 。 文中还提供了最先进跟踪器的分类和广泛的错误分析 。
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shred some light into potential future research directions.


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