Researchers have shrunk state-of-the-art computer vision models to run on low-power devices. Growing pains: Visual recognition is deep learning’s strongest skill. Computer vision algorithms are ...

Despite this, the researchers ay the algorithm has performed as well or better in object recognition tests than other leading object recognition algorithms. For example, the Evolution-Constructed ...

Driverless cars, for example, use computer vision and image recognition to identify pedestrians, signs, and other vehicles. For a deeper dive into computer vision check out the following: ...

Previous adversarial examples have largely been designed in “white box” settings, where computer scientists have access to the underlying mechanics that power an algorithm. In these scenarios ...

Computer vision algorithms usually rely on convolutional neural networks, or CNNs. CNNs typically use convolutional, pooling, ReLU, fully connected, and loss layers to simulate a visual cortex.

Many face recognition algorithms boast of classification accuracy scores over 90%, but these outcomes are not universal due to a problem called unbalanced datasets. Much of standard training databases ...