Tag Archives: Image Recognition
The two-year fight to stop Amazon from selling face recognition to the police
In the summer of 2018, nearly 70 civil rights and research organizations wrote a letter to Jeff Bezos demanding that Amazon stop providing face recognition technology to governments. As part of an increased focus on the role that tech companies were playing in enabling the US government’s tracking and deportation of immigrants, it called on Amazon to “stand up for civil rights and civil liberties.” “As advertised,” it said, “Rekognition is a powerful surveillance system readily available to violate rights and target communities of color.”
Along with the letter, the American Civil Liberties Union (ACLU) of Washington delivered over 150,000 petition signatures as well as another letter from the company’s own shareholders expressing similar demands. A few days later, Amazon’s employees echoed the concerns in an internal memo.
Despite the mounting pressure, Amazon continued with business as usual. Read More
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches,which have generally been supervised, the training objective typically measures a pixel-wise average distance be-tween the super-resolved (SR) and HR images. Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions. We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present a novel super-resolution algorithm addressing this problem, PULSE (Photo Up sampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training on databases of LR-HR image pairs for supervised learning). Instead of starting with the LR image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original LR image. This is formalized through the “down-scaling loss,” which guides exploration through the latent space of a generative model. By leveraging properties of high-dimensional Gaussians, we restrict the search space to guarantee that our outputs are realistic. PULSE thereby generates super-resolved images that both are realistic and downscale correctly. We show extensive experimental results demonstrating the efficacy of our approach in the do-main of face super-resolution (also known as face hallucination). Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously possible. Read More
Assessing the Big Five personality traits using real-life static facial images
There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using ‘selfies’. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality. Read More
Moscow uses facial recognition network to maintain quarantine
A vast and contentious network of facial recognition cameras keeping watch over Moscow is now playing a key role in the battle against the spread of the coronavirus in Russia.
The city rolled out the technology just before the epidemic reached Russia, ignoring protests and legal complaints over sophisticated state surveillance. Read More
Faster video recognition for the smartphone era
By one estimate, training a video-recognition model can take up to 50 times more data and eight times more processing power than training an image-classification model. That’s a problem as demand for processing power to train deep learning models continues to rise exponentially and concerns about AI’s massive carbon footprint grow. Running large video-recognition models on low-power mobile devices, where many AI applications are heading, also remains a challenge.
Song Han, an assistant professor at MIT’s Department of Electrical Engineering and Computer Science (EECS), is tackling the problem by designing more efficient deep learning models. Read More
AI that Generates Inspirational pictures..
Researchers use AI to deblur human faces in photos
We’ve all been there: You’re snapping pics with your phone — perhaps of a high-speed bike ride or of a hockey match — and don’t think to check whether the autofocus is in lockstep with the action. It isn’t, as you later discover, and you’re stuck with a gallery of unusably blurry photos.
In search of a solution, scientists at the Inception Institute of Artificial Intelligence in the United Arab Emirates, the Beijing Institute of Technology, and Stony Brook University developed an AI system that removes blur from images in post-production. Read More
The Secretive Company That Might End Privacy as We Know It
The New York Times has a long story about a little-known start-up, Clearview AI, that helps law enforcement match photos of unknown people to their online images — and “might lead to a dystopian future or something,” a backer says. Read More
#image-recognition, #privacySelf-training with Noisy Student improves ImageNet classification
We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 16.6% to 74.2%, reduces ImageNet-C mean corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 27.8 to 16.1.
To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as good as possible. But during the learning of the student, we inject noise such as data augmentation, dropout, stochastic depth to the student so that the noised student is forced to learn harder from the pseudo labels. Read More
Rick's Cafe AI 