picture
83ccb398f3ce9c4d137011f36a03c7d4-Paper-Conference.pdf
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to asemantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage notonly semantic smoothness butalsoboundary sharpness intheupsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation istogenerate similarity-awarekernels bycomparing thesimilarity between each encoder feature point and the spatially associated local region of decoder features.
- Asia > China (0.05)
- Asia > Middle East > Republic of Türkiye > Karaman Province > Karaman (0.04)
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. Extensive research efforts have been made to quantify and reduce predictive uncertainty. However, most existing works are designed for unimodal data, whereas multi-view uncertainty estimation has not been sufficiently investigated. Therefore, we propose a new multi-view classification framework for better uncertainty estimation and out-of-domain sample detection, where we associate each view with an uncertainty-aware classifier and combine the predictions of all the views in a principled way.
'Minecraft' movie mayhem raises alarms for America's youth, 'bad for society': expert
"A Minecraft Movie," the big-screen adaptation of the popular video game "Minecraft," has been packing theaters with rowdy kids and teens since its release this month, spurring a social media phenomenon and sparking concern for America's youth. Videos on social media show young theatergoers huge reactions to one key scene, where one of the film's stars, Jack Black, yells out the phrase "Chicken Jockey!" as a small, Frankenstein-looking creature lands on top of a chicken in a boxing ring to face off with co-star Jason Momoa. The scene has prompted excited fans to scream, shout, throw popcorn around, jump up out of their seats, and in one instance in Provo, Utah, toss a live chicken in the air during a screening, according to the Salt Lake Tribune. Springs Cinema & Taphouse in Sandy Springs, Georgia, told FOX 5 Atlanta that its staff has had to clean up popcorn, ICEEs, ketchup and shattered glass. The scene featuring the "Chicken Jockey" in "A Minecraft Movie" has spawned some chaotic movie theater behavior from young audiences. "The movie-going experience has changed a lot since I was younger," Josh Gunderson, director of marketing and events at Oviedo Mall in Florida, told FOX Business.
- North America > United States > Utah > Utah County > Provo (0.25)
- North America > United States > Georgia > Fulton County > Sandy Springs (0.25)
- Media > Film (1.00)
- Leisure & Entertainment > Games > Computer Games (1.00)
MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data
Berman, William, Peysakhovich, Alexander
We train a model to generate images from multimodal prompts of interleaved text and images such as "a
- Information Technology > Sensing and Signal Processing > Image Processing (1.00)
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
InfiniteNature-Zero: Fly Into Your Pictures With AI!
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Generate infinite new frames as if you would be flying into your image!
Watching TV is linked to dementia risk while computer can guard against it
Watching TV increases your risk of dementia – but using a computer can help protect against it, a study suggests. Researchers analysed 12 years of data on 150,000 people in the UK aged 60 or over. Those who developed dementia watched three hours, 24 minutes of TV a day. Those who did not watched three hours – but spent six minutes longer a day on the computer. Watching TV increases your risk of dementia – but using a computer can help protect against it, a study suggests.
- Europe > United Kingdom (0.28)
- North America > United States > California (0.21)
Demystifying Artificial Intelligence - Explained in One Picture - DataScienceCentral.com
This article was written by Swami Chandrasekaran. Click on picture to zoom in. When I wrote my blog post Becoming a Data Scientist-- Curriculum via Metromap, little did I know that it will receive a rousing feedback. Over years a lot of people reached out to me with very kind words and how they use it as a guide in their data scientist journey. Also, many who sought permission to use the Metromap picture in their presentations as well as a few universities that also reached out to use it as part of their syllabus.
Logistic Regression in One Picture - DataScienceCentral.com
Logistic regression is regressing data to a line (i.e. This type of regression is a good choice when modeling binary variables, which happen frequently in real life (e.g. The logistic regression model is popular, in part, because it gives probabilities between 0 and 1. Let's say you were modeling a risk of credit default: values closer to 0 indicate a tiny risk, while values closer to 1 mean a very high risk. The following image shows an example of how one might tailor a logistic model for credit score based risk.
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
The Riemann Hypothesis in One Picture - DataScienceCentral.com
I wrote this article for machine learning and analytic professionals in general. Actually, I describe a new visual, simple, intuitive method for supervised classification. It involves synthetic data and explainable AI. But at the same time, I describe in layman's terms the Riemann Hypothesis (RH). Also, I offer a new perspective on the subject for those who attempt to solve the most famous unsolved mathematical problem of all times.