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Modern computer vision systems have superhuman accuracy when it comes to image recognition and analysis, but they don’t really understand what they see. At IBM Research, we’re designing AI systems with the ability to see the world like we do.
Therefore, it is crucial to meticulously review, thoroughly explore, and comprehensively survey MU for computer vision (CV) through this tutorial. Within this tutorial, we will delve into the algorithmic foundations of MU methods, including techniques such as localization-informed unlearning, unlearning-focused finetuning, and vision model ...
In a pair of papers at this year’s Computer Vision and Pattern Recognition Conference , IBM researchers show that an image classifier pretrained on synthetic data and fine-tuned on real data for tasks, did as well as one trained exclusively on ImageNet’s database of real-world photos. Their classifier even proved capable of transferring its ...
IBM researchers check AI bias with counterfactual text. Get the latest research news in your inbox each month. Experiment and master the use of analog AI hardware for deep learning. A no-code sandbox to experiment with neural network design, and analog devices, and algorithmic optimizers to build high accuracy deep learning models.
In this paper, we connect technical insights from deep learning scaling laws and transfer learning with the economics of IT to propose a framework for estimating the cost of deep learning computer vision systems to achieve a desired level of accuracy.
Objective: We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD. Methods: We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues.
In this paper we discuss the evolution of computer-based object recognition systems over the last fifty years, and overview the successes and failures of proposed solutions to the problem.
Another team has applied deep learning methods to tackle problems traditionally solved with classical computer vision approaches. As deep learning models require large amounts of data for training, the team creates synthetic data that maximizes the accuracy of the models, enabling the AI to analyze challenging low-quality documents.
The IBM Research blog is the home for stories told by the researchers, scientists, and engineers inventing What’s Next in science and technology.
Visual Prompting is a paradigm shift in the field of computer vision: being able to build an accurate segmentation model in just a few seconds of work was unthinkable a few years ago. The benefit of this technology lies in its application in technical domains, where the available data is usually limited, and new models are needed on a daily basis.