Thursday, August 16, 2018
Search
  
Submit your own News for
inclusion in our Site.
Click here...
Breaking News
Samsung Exynos Modem 5100 Is the First 5G Modem Fully Compliant with 3GPP Standards
Samsung and Harman Kardon Provide Sound in New Premium Soundbar Lineup
Intel Discloses New Chip Security Flaws
Trendforce Confirms New iPhone Launch This Fall, Including a Budget Version
NVIDIA Unveils Turing Architecture, Quadro RTX Ray-Tracing GPU
AMD Says New 2nd Generation, 32-core AMD Ryzen Threadripper Processors Break Boundaries of High-End Desktop Market
HyperX Announces the HyperX Gaming microSD Cards
Fraunhofer Scientists Find Dangerous Security Holes in Tracker Apps
Active Discussions
Which of these DVD media are the best, most durable?
How to back up a PS2 DL game
Copy a protected DVD?
roxio issues with xp pro
Help make DVDInfoPro better with dvdinfomantis!!!
menu making
Optiarc AD-7260S review
cdrw trouble
 Home > News > General Computing > Twitter...
Last 7 Days News : SU MO TU WE TH FR SA All News

Thursday, January 25, 2018
Twitter Developing video Sharing Tool


Twitter Inc is developing a Snapchat-style tool that makes it simpler for users to post videos on its app, Bloomberg reported on Thursday.

The company already has a working demo of the camera-centered product, Bloomberg reported, citing people who have seen it.

With the new product video posting will be more easy and straightforward. Twitter users will not have to
open the Twitter app, click the compose button, find the camera button, take the video or picture, then click on the tweet button.

The goal of the new feature is to entice people to share video clips of what's happening around them.

The design as well as the timing of the product's launch has not been settled upon, the report added.

Twitter declined to comment.

Facebook has copied features from Snap Inc.'s Snapchat, a mobile app focused on photos and videos.

Machine learning to crop photos

In related news, Twitter says that machine learning to automatically crop picture previews to their most interesting part.

ML researcher Lucas Theis and ML lead Zehan Wang used a blog post to explain how they started just using facial recognition to crop images to faces, but found that this method didn't work with pictures of scenery, objects, and cats.

Their solution was "cropping using saliency". A region having high saliency means that a person is likely to look at it when freely viewing the image. Academics have studied and measured saliency by using eye trackers, which record the pixels people fixated with their eyes. In general, people tend to pay more attention to faces, text, animals, but also other objects and regions of high contrast. This data can be used to train neural networks and other algorithms to predict what people might want to look at.

The basic idea is to use these predictions to center a crop around the most interesting region

Thanks to recent advances in machine learning, saliency prediction has gotten a lot better. Unfortunately, the neural networks used to predict saliency are too slow to run in production, since we need to process every image uploaded to Twitter and enable cropping without impacting the ability to share in real-time. On the other hand, Twitter doesn't need fine-grained, pixel-level predictions, since we are only interested in roughly knowing where the most salient regions are. In addition to optimizing the neural network's implementation, Twitter used two techniques to reduce its size and computational requirements.

First, the company used a technique called knowledge distillation to train a smaller network to imitate the slower but more powerful network. With this, an ensemble of large networks is used to generate predictions on a set of images. These predictions, together with some third-party saliency data, are then used to train a smaller, faster network.
Twitter says it drastically reduced the size of saliency prediction networks through a combination of knowledge distillation and pruning.

Second, the company developed a pruning technique to iteratively remove feature maps of the neural network which were costly to compute but did not contribute much to the performance. To decide which feature maps to prune, Twitter computed the number of floating point operations required for each feature map and combined it with an estimate of the performance loss that would be suffered by removing it.

Together, these two methods allowed Twitter to crop media 10x faster than just a vanilla implementation of the model and before any implementation optimizations.

These updates are currently in the process of being rolled out to everyone on twitter.com, iOS and Android.



Previous
Next
Verbatim Releases New Speedy External SSD        All News        Alphabet Launches Chronicle Cybersecurity Company
Apple is Working on New Digital Book Reader for iPhones, iPads     General Computing News      Alphabet Launches Chronicle Cybersecurity Company

Get RSS feed Easy Print E-Mail this Message

Related News
Twitter Reports Revenue But Monthly Usage Falls
Twitter, Facebook, Alphabet to Testify at U.S. House hearing
Twitter Targets Fake Accounts
Facebook, Twitter Release New Tools to Track Advertising
Twitter to Tackle Abuse and Malicious Bots
Updated Twitter App Highlights Big events, News Stories
Twitter and Facebook Reveal Measures to Bring Transparency to Political Ads
Twitter to Reduce Disruptive Accounts
Twitter Urges 330 million Users to Change Passwords After Internal Leak
Twitter Increases User Base, Ad Revenue
Social Networks Have One Hour to Remove Online Terrorist Content, Europe Says
Twitter Makes it Easier to Save and Share Tweets

Most Popular News
 
Home | News | All News | Reviews | Articles | Guides | Download | Expert Area | Forum | Site Info
Site best viewed at 1024x768+ - CDRINFO.COM 1998-2018 - All rights reserved -
Privacy policy - Contact Us .