Computer Vision

Researchers at Meta and the University of Texas at Austin Propose ‘Detic’: A Method to Detect Twenty-Thousand Classes using Image-Level Supervision

AI Paper Summary
The difficulty of object detection is divided into two parts: detecting the object (localization) and labeling it (classification). Traditional techniques rely on box labels...

Researchers Introduce ‘SeMask’: An Effective Transformer-Framework That Incorporates Semantic Information Into The Encoder With The Help Of A Semantic Attention Operation

AI Paper Summary
After demonstrating the transformer’s efficiency in the visual domain, the research community has focused on extending its use to several fields. One of these...

Google AI Introduces V-MoE: A New Architecture For Computer Vision Based On A Sparse Mixture Of Experts

AI Paper Summary
Throughout the previous few decades, deep learning advances have contributed to outstanding outcomes on a wide range of tasks, including image classification, machine translation,...

Researchers From NVIDIA & Vanderbilt University Propose ‘Swin UNETR’: A Novel Architecture for Semantic Segmentation of Brain Tumors Using Multi-Modal MRI Images

AI Paper Summary
The human brain is affected by about 120 different forms of brain tumors. AI-based intervention for tumor identification and surgical pre-assessment is on the...

CMU Researchers Propose A Computer Vision-Based Approach With Data-Frugal Deep Learning To Optimize Microstructure Imaging

AI Paper Summary
Materials processing is the process of turning raw materials into final items through a sequence of phases or "unit operations." The activities entail a...

Researchers From China Propose A Pale-Shaped Self-Attention (PS-Attention) And A General Vision Transformer Backbone, Called Pale Transformer

AI Paper Summary
Transformers have recently demonstrated promising performance in a variety of visual tests. Inspired by Transformer's success on a wide range of NLP tasks, Vision...

Apple ML Researchers Introduce ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

AI Paper Summary
Understanding indoor 3D scenes are becoming increasingly important in augmented reality, robotics, photography, games, and real estate. Many state-of-the-art scene interpretation algorithms have lately...

Efficient Large-scale Object Counting in Satellite Images with Importance Sampling

AI Paper Summary
Object counts provide insights into socioeconomic and environmental issues: for example, building counts reflect the level of urbanization. How can we estimate the total...

Meta AI and CMU Researchers Present ‘BANMo’: A New Neural Network-Based Method To Build Animatable 3D Models From Videos

AI Paper Summary
Previous work on articulated 3D shape reconstruction has frequently relied on specialized sensors (e.g., synchronized multi-camera systems) or pre-built 3D deformable models (e.g., SMAL...

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

AI Paper Summary
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

AI Paper Summary
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

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