Computer Vision

Researchers From Stanford and NVIDIA Introduce A Tri-Plane-Based 3D GAN Framework To Enable High-Resolution Geometry-Aware Image Synthesis

Generative Adversarial Networks (GANs) have been one of the main hypes of recent years. Based on the famous generator-discriminator mechanism, their very simple functioning...

Researchers Introduce A New Hand Gesture Recognition Algorithm Combining Hand-Type Adaptive Algorithm And Effective-Area Ratio For Efficient Edge Computing

Almost all of our computer interaction occurs via mouse, keyboards, and touch screens. An essential step in making human-computer interactions more efficient would be...

ETH Zurich Team Introduce Exemplar Transformers: A New Efficient Transformer Layer For Real-Time Visual Object Tracking

Visual tracking involves estimating the trajectory of an object in a video series, which is one of the fundamental challenges in computer vision. With...

Researchers Develop ‘Garment4D’: A Garment Reconstruction Model Using Point Cloud Sequences

Virtual try-on, virtual reality/augmented reality, and visual effects all use garment reconstruction. With the use of implicit representation or volumetric representation, extensive efforts have...

OpenAI Introduces ‘GLIDE’ Model For Photorealistic Image Generation

Images, such as graphics, paintings, and photographs, may frequently be explained in language, but they might also take specific talents and hours of effort...

Researchers from the University of Chicago and Tel Aviv University Introduce ‘Text2Mesh’: A Novel Framework to Alter both Color and Geometry of 3D Meshes...

In recent years, neural-based generative models have been at the center of attention for their exceptional capability of creating aesthetically attractive graphical content seemingly...

AI Researchers Propose An Easy-To-Use Federated Learning Framework Called ‘FedCV’ For Diverse Computer Vision Tasks

Federated Learning (FL) is a distributed learning paradigm that can learn a global or a personalized model for each user relying on decentralized data...

MIT Researchers Propose Patch-Based Inference to Reduce the Memory Usage for Tiny Deep Learning

Machine learning provides researchers with excellent tools for identifying and predicting patterns and behavior. These tools are also capable of learning, optimizing, and performing...

Google AIā€™s ‘TokenLearner’ Can Improve Vision Transformer Efficiency And Accuracy

Transformer models consistently obtain state-of-the-art computer vision tasks, including object detection and video classification. In standard convolutional approaches, images are processed pixel-by-pixel. To obtain...

AI Researchers Propose ‘GANgealing’: A GAN-Supervised Algorithm That Learns Transformations of Input Images to Bring Them into Better Joint Alignment

The correspondence problem of visual alignment is one that computer vision algorithms must solve for many different applications.It's considered a critical element in Optical...

Researchers from Sea AI Lab and National University of Singapore Introduce ‘PoolFormer’: A Derived Model from MetaFormer for Computer Vision Tasks

The main hype of the last few years in the world of Deep Learning is definitely Transformers. Since their advent in 2017 with the...

Facebook AI and University of Guelph Open-Sources Graph HyperNetworks (GHN-2): A Meta-Model That Predicts Starting Parameters For Deep-Learning Neural Networks

In machine learning pipelines, deep learning has proved successful in automating feature design. However, many researchers reveal that the techniques for improving neural network...

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