CIFAR-100
CIFAR-100 is a dataset widely used in machine learning and computer vision, consisting of 60,000 images across 100 classes.
computer vision
CIFAR-10
CIFAR-10 is a widely used dataset in machine learning for image classification, consisting of 60,000 32×32 color images in 10 different classes.
Ego4D (first-person video dataset)
Ego4D is a large-scale dataset designed for first-person video understanding, aimed at enhancing machine perception through diverse real-world scenarios.
DensePose (dense human pose estimation)
DensePose is a technology for estimating human body poses in images and videos, mapping them to a 3D surface model.
SSD (object detection)
SSD (Single Shot MultiBox Detector) is a popular deep learning model for object detection that performs object localization and classification in a single forward pass, enabling real-time applications. It was introduced to balance accuracy and speed in detecting multiple objects within images.
Fei-Fei Li
Fei-Fei Li is a prominent computer scientist known for her pioneering work in artificial intelligence and computer vision. She has contributed significantly to the development of image recognition technologies and has held influential academic and industry positions.
CenterPoint (center-based 3D detection)
CenterPoint is a 3D object detection method that localizes objects by detecting their centers in point cloud data. It is designed for autonomous driving and robotics applications, improving detection accuracy and efficiency.
SwAV (swapping assignments between views)
SwAV is a self-supervised learning method in computer vision that enables effective learning by swapping assignments between different views of data.
BEVDet
BEVDet is a deep learning framework designed for 3D object detection using bird’s-eye view representations. It is primarily used in autonomous driving systems to improve the perception of surrounding environments from multi-camera setups.
PCT (point cloud transformer)
PCT (point cloud transformer) is a deep learning architecture designed to process and analyze 3D point cloud data using transformer mechanisms. It enables efficient feature extraction and understanding of spatial relationships in unstructured 3D data for various applications.