3D generation
3D generation is the process of using artificial intelligence (AI) to automatically create three-dimensional (3D) models from various inputs like text descriptions or images, bypassing traditional manual modeling. These advanced AI models, often employing techniques like deep learning, can generate complex 3D structures in formats such as meshes or point clouds for use in fields like gaming, augmented reality (AR), and 3D printing.
3D generation is a key task in computer vision. Below you will find the standard benchmarks used to evaluate models, along with current state-of-the-art results.
Benchmarks & SOTA
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Few-Shot Image Classification
Image classification with limited labeled examples per class (few-shot learning). Models are evaluated on their ability to classify images into categories with only a handful of training examples (typically 1-10) per class.
Open-Vocabulary Object Detection
Object detection with open vocabulary - detecting objects from arbitrary text descriptions without being limited to a fixed set of categories.
Object counting
Object counting in AI is a computer vision task that uses machine learning and image processing to identify and enumerate distinct objects within digital images and videos. It can differentiate between various object types, sizes, and shapes, even in crowded or dynamically changing scenes. The process typically involves object detection using deep learning models like convolutional neural networks (CNNs) to recognize and localize objects, followed by aggregation to provide a total count. This technology is applied in fields like manufacturing for quality control and production monitoring.
Video segmentation
Video segmentation is the task of partitioning video frames into multiple segments or objects. Unlike image segmentation which works on static images, video segmentation tracks objects across frames in a video sequence.
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