Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

This project is a baseline in the activity classification and its temporal location, focused on the ActivityNet Challenge. Here is detailed all the process of our proposed pipeline, as well the trained models and the utility to classify and temporally localize activities on new videos given. All the steps have been detailed, from downloading the dataset, to predicting the temporal locations going through the feature extraction and also the training.

https://imatge-upc.github.io/activitynet-2016-cvprw/
https://github.com/imatge-upc/activitynet-2016-cvprw

https://www.youtube.com/watch?v=3G-Vdmsluw0

#keras #video #activity #cvprw

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