32,859 research outputs found
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Development of techniques to enhance man/machine communication
A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied
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Pattern-driven security, privacy, dependability and interoperability management of iot environments
Achieving Security, Privacy, Dependability and Interoperability (SPDI) is of paramount importance for the ubiquitous deployment and impact maximization of Internet of Things (IoT) applications. Nevertheless, said requirements are not only difficult to achieve at system initialization, but also hard to prove and maintain at run-time. This paper highlights an approach to tackling the above challenges, through the definition of pattern language and a framework that can guarantee SPDI in IoT orchestrations. By integrating pattern reasoning engines at the various layers of the IoT infrastructure, and a machine-processable representation of said pattern through Drools rules, the proposed framework can provide ways to fulfill SPDI requirements at design time, and also provide the means to guarantee those SPDI properties and manage the orchestrations accordingly. Moreover, an application example of the framework is presented in an Industrial IoT monitoring environment
Reinforced Video Captioning with Entailment Rewards
Sequence-to-sequence models have shown promising improvements on the temporal
task of video captioning, but they optimize word-level cross-entropy loss
during training. First, using policy gradient and mixed-loss methods for
reinforcement learning, we directly optimize sentence-level task-based metrics
(as rewards), achieving significant improvements over the baseline, based on
both automatic metrics and human evaluation on multiple datasets. Next, we
propose a novel entailment-enhanced reward (CIDEnt) that corrects
phrase-matching based metrics (such as CIDEr) to only allow for
logically-implied partial matches and avoid contradictions, achieving further
significant improvements over the CIDEr-reward model. Overall, our
CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.Comment: EMNLP 2017 (9 pages
Automatic Detection of Online Jihadist Hate Speech
We have developed a system that automatically detects online jihadist hate
speech with over 80% accuracy, by using techniques from Natural Language
Processing and Machine Learning. The system is trained on a corpus of 45,000
subversive Twitter messages collected from October 2014 to December 2016. We
present a qualitative and quantitative analysis of the jihadist rhetoric in the
corpus, examine the network of Twitter users, outline the technical procedure
used to train the system, and discuss examples of use.Comment: 31 page
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