Automatically identifying the overall sentiment expressed in a video or text could be useful for a wide range of applications. For instance, it could help companies or political parties to screen large amounts of online content and gain insight on what the public thinks about their products, services, campaigns or initiatives.
Researchers at University of Padua, the Open University and University of Copenhagen have recently introduced a new framework for video sentiment analysis that is based on quantum physics theory. Read More
Tag Archives: Image Recognition
Machine Learning & Image to Audio Captioning
A brief literature review of how machine learning is used to translate images directly into speech. Read More
How to build an image automatic rotator in 24 hours
The simplicity of Neural Network and Keras’ tools.
Recently, I was challenged to do this task which basically asked to use neural networks to predict the image orientation (upright, upside down, left or right) and with that prediction rotate the image to the correct position (upright), all of this in 24 hours! Read More
YouTube removes record number of videos as human moderators replaced by AI
YouTube’s automated filters were less reliable than human moderators, but the company ‘accepted a lower level of accuracy; to ensure harmful content was removed.
YouTube has removed more videos in the second quarter of 2020 than ever before.
During the coronavirus pandemic, when the video sharing site could not rely on its human moderators as much as previously, YouTube increased its use of automated filters in order to take down videos that could potentially violate its policies. Read More
Facebook and NYU use artificial intelligence to make MRI scans four times faster
AI learns to create MRI scans from a quarter of the data.
If you’ve ever had an MRI scan before, you’ll know how unsettling the experience can be. You’re placed in a claustrophobia-inducing tube and asked to stay completely still for up to an hour while unseen hardware whirs, creaks, and thumps around you like a medical poltergeist. New research, though, suggests AI can help with this predicament by making MRI scans four times faster, getting patients in and out of the tube quicker.
The work is a collaborative project called fastMRI between Facebook’s AI research team (FAIR) and radiologists at NYU Langone Health. Read More
Denis Shiryaev used AI to remaster the oldest recorded video, “Roundhay Garden Scene”, England,1888
New AI Dupes Humans into Believing Synthesized Sound Effects Are Real
Using machine-learning, AutoFoley determines what actions are taking place in a video clip and creates realistic sound effects.
… Researchers have created an automated program that analyzes the movement in video frames and creates its own artificial sound effects to match the scene. In a survey, the majority of people polled indicated that they believed the fake sound effects were real. The model, AutoFoley, is described in a study published June 25 in IEEE Transactions on Multimedia. Read More
AI Magic Makes Century-Old Films Look New
Denis Shiryaev uses algorithms to colorize and sharpen old movies, bumping them up to a smooth 60 frames per second. The result is a stunning glimpse at the past.
On April 14, 1906, the Miles brothers left their studio on San Francisco’s Market Street, boarded a cable car, and began filming what would become an iconic short movie. Called A Trip Down Market Street, it’s a fascinating documentation of life at the time.
… Well over a century later, an artificial intelligence geek named Denis Shiryaev has transformed A Trip Down Market Street into something even more magical. Read More
The hack that could make face recognition think someone else is you
Researchers have demonstrated that they can fool a modern face recognition system into seeing someone who isn’t there.
A team from the cybersecurity firm McAfee set up the attack against a facial recognition system similar to those currently used at airports for passport verification. By using machine learning, they created an image that looked like one person to the human eye, but was identified as somebody else by the face recognition algorithm—the equivalent of tricking the machine into allowing someone to board a flight despite being on a no-fly list. Read More
Generative Pretraining from Pixels
Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Trans-former to autoregressively predict pixels, without incorporating knowledge of the 2D input structure.Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pretrained models. An even larger model trained on a mixture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet,achieving 72.0% top-1 accuracy on a linear probe of our features. Read More
#image-recognition
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