Nikolay Ironov had been working as a graphic designer for more than a year before he revealed his secret.
As an employee of Art. Lebedev Studio — Russia’s largest design company — Ironov had already worked on more than 20 commercial projects, creating everything from beer bottle labels to startup logos.
But Ironov was not the person he claimed to be. In fact, the designer was not a person at all. Read More
Tag Archives: Image Recognition
Deepfake used to attack activist couple shows new disinformation frontier
Oliver Taylor, a student at England’s University of Birmingham, is a twenty-something with brown eyes, light stubble, and a slightly stiff smile.
Online profiles describe him as a coffee lover and politics junkie who was raised in a traditional Jewish home. His half dozen freelance editorials and blog posts reveal an active interest in anti-Semitism and Jewish affairs, with bylines in the Jerusalem Post and the Times of Israel.
The catch? Oliver Taylor seems to be an elaborate fiction. Read More
Google’s quiet experiments may lead to smart tattoos, holographic glasses
A simple pair of sunglasses that projects holographic icons. A smartwatch that has a digital screen but analog hands. A temporary tattoo that, when applied to your skin, transforms your body into a living touchpad. A virtual reality controller that lets you pick up objects in digital worlds and feel their weight as you swing them around. Those are some of the projects Google has quietly been developing or funding, according to white papers and demo videos, in an effort to create the next generation of wearable technology devices. Read More
Image Search with Text Feedback by Visiolinguistic Attention Learning
Image search with text feedback has promising impacts in various real-world applications, such as e-commerce and internet search. Given a reference image and text feedback from user, the goal is to retrieve images that not only resemble the input image, but also change certain aspects in accordance with the given text. This is a challenging task as it requires the synergistic understanding of both image and text. In this work, we tackle this task by a novel Visiolinguistic Attention Learning (VAL) framework. Specifically, we propose a composite transformer that can be seamlessly plugged in a CNN to selectively preserve and transform the visual features conditioned on language semantics. By inserting multiple composite transformers at varying depths,VAL is incentive to encapsulate the multi-granular visiolinguistic information, thus yielding an expressive representation for effective image search. We conduct comprehensive evaluation on three datasets: Fashion200k, Shoes and FashionIQ. Extensive experiments show our model exceedsexisting approaches on all datasets, demonstrating consistent superiority in coping with various text feedbacks, including attribute-like and natural language descriptions. Read More
Improving Robustness of Deep-Learning-Based Image Reconstruction
Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples. These examples make deployment of such models in safety-critical tasks questionable.Use of deep neural networks as inverse problem solvers has generated much excitement for medical imaging including CT and MRI, but recently a similar vulnerability has also been demonstrated for these tasks. We show that for such inverse problem solvers, one should analyze and study the effect of adversaries in the measurement-space,instead of the signal-space as in previous work. Read More
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs),resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set of transformations from the data and instead frees up model capacity, allowing more discriminative features to be learned. This reduction in the number of required parameters also reduces the risk of over-fitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework. Each filter is defined as a linear combination of steerable basis filters,enabling exact rotation and decreasing the number of trainable parameters compared to standard filters. We also provide the first in-depth comparison of different rotation-equivariant CNNs for histology image analysis and demonstrate the advantage of encoding rotational symmetry into modern architectures. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology: breast tumour classification, colon gland segmentation and multi-tissue nuclear segmentation. Read More
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster R-CNN baseline on the challenging COCO object detection dataset. More-over, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. Read More
Why it matters that IBM is getting out of the facial recognition business
The news that IBM will no longer produce facial recognition technology might not sound huge at first. The company’s commitment to opposing this type of racially biased surveillance technology fits into a welcome trend of actions being taken after anti-police brutality protests have swept the nation. Although some are already warning that IBM’s move won’t end the age of facial recognition, others say it’s a significant step in the right direction. Read More
AI RT Artist uses AI to create realistic-looking portraits of famous figures including Napoleon and Van Gogh
AN ARTIST has used artificial intelligence to create human-like portraits from statues and paintings of famous faces.
If you’ve ever wondered whatthe Statue of Liberty or Michelangelo’s David statue would look like as real people then take a look. Read More
For AI, data are harder to come by than you think
AMAZON’S “GO” STORES are impressive places. The cashier-less shops, which first opened in Seattle in 2018, allow app-wielding customers to pick up items and simply walk out with them. The system uses many sensors, but the bulk of the magic is performed by cameras connected to an AI system that tracks items as they are taken from shelves. Once the shoppers leave with their goods, the bill is calculated and they are automatically charged. Read More
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