FACE DETECTION BY LITERATURE
Обзор и бенчмарки различных методов детектирования лиц
#facedetection #cv
http://www.erogol.com/face-detection-networks-literature/

Блог Kaggle: Что мы читаем, 15 Любимых Data Science ресурсов

http://blog.kaggle.com/2016/09/13/what-were-reading-data-science-resources/

Google, Facebook, Amazon объединяют силы для развития искусственного разума

http://www.bbc.com/russian/news-37503129

YouTube-8M: датасет, это как ImagNet только для видео!
4800 классов и 8 миллионов видео

YouTube-8M is a large-scale labeled video dataset that consists of 8 million YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities. It also comes with precomputed state-of-the-art vision features from billions of frames, which fit on a single hard disk. This makes it possible to train video models from hundreds of thousands of video hours in less than a day on 1 GPU!

https://research.google.com/youtube8m/
http://arxiv.org/pdf/1609.08675v1.pdf

#dataset

Large Scale Movie Denoscription and Understanding Challenge (LSMDC), at ECCV 2016

The challenge will be presented at the "Joint 2nd Workshop on Storytelling with Images and Videos (VisStory) and Large Scale Movie Denoscription and Understanding Challenge (LSMDC 2016)" in conjunction with ECCV 2016, Amsterdam, The Netherlands.

Презентации с ML/DL секции Russian Supercomputing Days:
технологические аспекты от Mikhail Burtsev и Dmitry Korobchenko + обзор инвестиционной среды от Russia.AI

http://www.russia.ai/single-post/2016/10/10/Deep-Learning-%E2%80%93-Present-and-Future-of-AI-Slides-from-Russian-Supercomputing-Days-Conference

Лекция 1. Примеры применения анализа данных, стандартные задачи и методы

Обзор курсов по Deep Learning

Последнее время все больше и больше достижений в области искусственного интеллекта связано с инструментами глубокого обучения или deep learning. Мы решили разобраться, где же можно научиться необходимым навыкам, чтобы стать специалистом в этой области.

Clockwork Convnets for Video Semantic Segmentation.

Adaptive video processing by incorporating data-driven clocks.

We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video.

https://arxiv.org/pdf/1608.03609v1.pdf
https://github.com/shelhamer/clockwork-fcn

http://www.gitxiv.com/posts/89zR7ATtd729JEJAg/clockwork-convnets-for-video-semantic-segmentation

#Caffe #video #Segmentation