Instance Segmentation
Instance segmentation is a computer vision task that identifies each object in an image and delineates its exact pixel boundary. Unlike semantic segmentation, it distinguishes between individual instances of the same class.
Understanding Instance Segmentation
Instance segmentation is a computer vision task that combines object detection and semantic segmentation to identify and delineate each individual object in an image at the pixel level. Unlike semantic segmentation, which labels every pixel with a class but does not distinguish between separate objects of the same class, instance segmentation assigns a unique identity to each detected object. Architectures like Mask R-CNN and YOLACT have become standard approaches, building on convolutional neural networks and feature pyramid networks. Instance segmentation is crucial in applications such as autonomous driving where each pedestrian and vehicle must be tracked independently, in medical imaging for counting and measuring individual cells, and in robotics for object manipulation. The task is closely related to panoptic segmentation, which unifies instance and semantic segmentation into a single comprehensive scene understanding framework.
Category
Computer Vision
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Bounding Box
A bounding box is a rectangular border drawn around an object in an image to indicate its location and extent. Bounding boxes are the primary output format for object detection models.
Computer Vision
Computer vision is a field of AI that enables machines to interpret and understand visual information from images and videos. Applications include facial recognition, autonomous driving, medical imaging, and augmented reality.
Face Recognition
Face recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features in images or video. It is used in security systems, phone unlocking, and photo organization.
Image Captioning
Image captioning is the AI task of generating natural language descriptions of images. It requires both visual understanding (computer vision) and text generation (NLP) capabilities.
Image Classification
Image classification is the computer vision task of assigning a label to an entire image based on its visual content. Deep learning models like ResNet and Vision Transformers achieve near-human accuracy on this task.
Image Segmentation
Image segmentation is the process of partitioning an image into meaningful regions or classifying each pixel into a category. It is used in medical imaging, autonomous driving, and satellite analysis.
Masked Autoencoder
A masked autoencoder is a self-supervised learning method that masks random patches of an image and trains the model to reconstruct them. It has proven highly effective for pre-training vision models.
Neural Radiance Field
A Neural Radiance Field (NeRF) is a deep learning method that represents 3D scenes as continuous functions, enabling photorealistic novel view synthesis from 2D images. NeRFs have transformed 3D reconstruction and rendering.