Computer Vision - IBM Research
2023年2月15日 · 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.
Machine Unlearning in Computer Vision: Foundations and …
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 ...
IBM Research Israel Academic Collaboration - Computer Vision
Computer Vision . Our research interests include learning with limited labels, cross-domain, self-supervised and multi-modal learning, and modern model architectures. We focus on innovative state-of-the-art research that makes a difference. Read more about Computer Vision at IBM Research - Israel
Challenges in post-training quantization of Vision Transformers
Vision Transformers recently showed outstanding performance in computer vision tasks. However, those models are compute and memory intensive that require accelerators with a large amount of memory like NVIDIA A100 graphic processing unit for training and even for inference.
Data- and model-driven multiresolution processing for Computer …
We argue that this kind of multiresolution processing is not only useful in limiting the computational time, as has been widely recognized, but also can be a deciding factor in making the entire vision problem a tractable and stable one.
A new way to generate synthetic data for pretraining computer …
2022年6月23日 · 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 ...
Visual Prompting - IBM Research
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.
Automated computer vision assessment of hypomimia in …
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.
A low power, fully event-based gesture recognition system - IBM …
Abstract. We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS).
RegionViT: Regional-to-Local Attention for Vision Transformers
Vision transformer (ViT) has recently shown its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification. However, vanilla ViT simply inherits the same architecture from the natural language processing directly, which is often not optimized for vision applications.