AI-powered camera spotting a damaged product
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Assembly Line AI: How companies are using AI to spot manufacturing flaws

Computer vision has been learning how to spot manufacturing flaws. The pandemic is accelerating that education. Companies like Instrumental and Elementary are making AI-powered cameras that automate the spotting of damaged or badly assembled products on factory assembly lines.
Examples of detection of animals in images using Detection Transformer (DETR).
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Computer Vision Transformed: Google's Detection Transformer (DETR) for object detection

The transformer architecture that has shaken up natural language processing may replace recurrent layers in object detection networks. A Facebook team led by Nicolas Carion and Francisco Massa simplified object detection pipelines by using transformers, yielding Detection Transformer (DETR).
Goalkeeper
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Game Changer: Top football clubs are using AI to improve performance.

Football clubs are turning to computer vision for winning insights. Acronis, a Swiss cloud storage and security company, offers AI services designed to give a boost to some of the world’s top football clubs (soccer teams, to Americans), Wired reported.
Images and graphs related to Dense Steerable Filter CNN (DSF-CNN)
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Seeing Straight at Any Rotation: Dense Steerable Filter CNN (DSF-CNN) identifies rotated images.

A cat rotated by any number of degrees is still a cat. It takes a lot of rotated training images to teach convolutional filters this simple fact. A new filter design has this common-sense knowledge built-in.
Neural network tracking the body position of chimpanzees
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Model See, Model Do: Researchers use DensePose to analyze animal behavior.

Scientists who study animal behavior spend endless hours observing and taking notes about a creature’s actions and reactions. Computer vision could automate much of that work.
Illustration of a broken heart with a smirk in the middle
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Outing Hidden Hatred: How Facebook built a hate speech detector

Facebook uses automated systems to block hate speech, but hateful posts can slip through when seemingly benign words and pictures combine to create a nasty message. The social network is tackling this problem by enhancing AI’s ability to recognize context.
Data and information related to shortcut learning
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When Models Take Shortcuts: The causes of shortcut learning in neural networks

Neuroscientists once thought they could train rats to navigate mazes by color. Rats don’t perceive colors at all. Instead, they rely on the distinct odors of different colors of paint. New work finds that neural networks are prone to this sort of misalignment between training goals and learning.
Dishwashing robot working
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AI Does the Dishes: Dishcraft Robotics automatically cleans dishes and utensils.

A pioneer in dishwashing robots is reaching into commercial kitchens. Dishcraft Robotics uses machines equipped with computer vision to scrub dirties for corporate food services and, soon, restaurants.
Data related to YOLOv4
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Another Look at YOLO: How YOLOv4 is different from earlier versions

The latest update of the acclaimed real-time object detector You Only Look Once is more accurate than ever. Researchers at Taiwan’s Institute of Information Science Academia Sinica offer YOLOv4 — the first version not to include the architecture’s original creators.
Screen captures from videos generated by VidPress
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Text to Video in Two Minutes: Baidu's VidPress generates video from text.

Will reading soon become obsolete? A new system converts text articles into videos. VidPress, a prototype project from Chinese tech giant Baidu, currently generates more than 1,000 narrated video summaries of news stories daily.
Data related to few-shot learning
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Small Data the Simple Way: A training technique that can outperform few-shot learning

Few-shot learning seeks to build models that adapt to novel tasks based on small numbers of training examples. This sort of learning typically involves complicated techniques, but researchers achieved state-of-the-art results using a simpler approach.
False information about Covid-19 on Facebook posts
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Information Warfare Covid Edition: Facebook used humans and AI to spot false Covid claims.

Facebook’s AI can’t spot Covid-19 disinformation on its own. But with human help, it can slow the spread. Facebook uses a combination of humans and neural nets to crack down on messages that make false claims about Covid-19, which may have deadly consequences.
Excerpts from Newspaper Navigator
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An Archive Unearthed: Newspaper Navigator indexes visual elements in archival text.

An algorithm indexed photos, ads, and other images embedded in 170 years of American newspapers. Created by researchers at the University of Washington and U.S. Library of Congress, Newspaper Navigator uses object recognition to organize visual features of newspapers dating back to 1789.
Data and graphs related to teacher networks
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Flexible Teachers, Smarter Students: Meta Pseudo Labels improves knowledge distillation.

Human teachers can teach more effectively by adjusting their methods in response to student feedback. It turns out that teacher networks can do the same.
Animated pie chart
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Triage for Pandemic Patients: Complications AlgoMarker measures a patient's Covid risk.

Israeli and American hospitals are using an algorithm to flag individuals at high risk for Covid-19 complications.

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