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Deep Learning based image colorization with OpenCV - CV-Tricks.com
In India, we celebrated the festival of color "Holi" last week. We celebrate the end of the winter with a splash of color because that's what the spring will bring us in a few days. When I was young, the celebrations were sparse. It was the decade of frugal parenting. We waited for festivals so eagerly because it meant parent approved outing and fun.
How Artificial Intelligence Will Make Digital Humans Hollywood's New Stars
Script supervisors, editors, CG artists and actors all had better look out: "It's all over by 2045 -- we are no longer running the show." Following Paul Walker's death in 2013, the Furious 7filmmakers faced the delicate task of finishing the film using a digitally created version of the actor. To do so, they resorted to performance capture with the help of the actor's brothers, Caleb and Cody, as well as painstaking computer animation from a team at VFX house Weta. That was state-of-the-art in 2014. But imagine if, instead, a computer could have stepped in, watched all of Walker's performances in the previous Furious films, learning the minute details of how he walked, talked and even raised an eyebrow. And then imagine that artificial intelligence took over and itself helped to create a digital performance for Walker's character.
Ink Lines and Machine Learning
Pav Grochola is a Effects Supervisor at Sony Pictures Imageworks (SPI) and was co-effects supervisor on the Oscar winning Spiderman: Into the Spiderverse (along with Ian Farnsworth). He was tasked with solving how to produce natural looking line work for the film. A critical visual component for successfully achieving the comic book illustrative style, in CGI, was the creation of line work or "ink lines". SPI in testing discovered any approach that involves creating ink lines based on procedural "rules" (for example toon shaders) were ineffective in achieving the natural look that was wanted. The fundamental problem is that artists simply do not draw based on limited'rule sets' or guidelines.
IBM's AI performs state-of-the-art broadcast news captioning
Two years ago, researchers at IBM claimed state-of-the-art transcription performance with a machine learning system trained on two public speech recognition data sets, which was more impressive than it might seem. The AI system had to contend not only with distortions in the training corpora's audio snippets, but with a range of speaking styles, overlapping speech, interruptions, restarts, and exchanges among participants. In pursuit of an even more capable system, researchers at the Armonk, New York-based company recently devised an architecture detailed in a paper ("English Broadcast News Speech Recognition by Humans and Machines") that will be presented at the International Conference on Acoustics, Speech, and Signal Processing in Brighton this week. They say that in preliminary experiments it achieved industry-leading results on broadcast news captioning tasks. The system came with its own set of challenges, like audio signals with lots of background noise and presenters speaking on a wide variety of news topics.
The future of AI is collaborative
Jordan French is a multi-media journalist on the editorial staff at TheStreet.com He is also the Founder and Executive Editor at Grit Daily News. Formerly an engineer and attorney he represented the "People of the United States" in energy market manipulation cases as an enforcement attorney at the Federal Energy Regulatory Commission. As an engineer he worked on the Mars Gravity Biosatellite Program and later co-founded BeeHex, Inc., the personalized nutrition and robotics company that popularized 3D-printed pizza. The author of forthcoming book, The Gritty Entrepreneur, he is a frequent public speaker, technology evangelist and media moderator.
Bivariate Beta LSTM
Song, Kyungwoo, Jang, JoonHo, Shin, Seung jae, Moon, Il-Chul
Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing multi-modality or skewness. Besides, the previous models lack correlation modeling between the gates, which would be a new method to adopt domain knowledge. This paper proposes a new gate structure with the bivariate Beta distribution. The proposed gate structure enables hierarchical probabilistic modeling on the gates within the LSTM cell, so the modelers can customize the cell state flow. Also, we observed that our structured flexible gate modeling is enabled by the probability density estimation. Moreover, we theoretically show and empirically experiment that the bivariate Beta distribution gate structure alleviates the gradient vanishing problem. We demonstrate the effectiveness of bivariate Beta gate structure on the sentence classification, image classification, polyphonic music modeling, and image caption generation.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
Mihaylova, Tsvetomila, Karadjov, Georgi, Atanasova, Pepa, Baly, Ramy, Mohtarami, Mitra, Nakov, Preslav
We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022