Various neural network architectures designed for vision tasks.
Examples: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models.
Use Cases: Image classification, style transfer, and feature extraction.
Identifying and verifying individuals based on facial features.
Example: Unlocking a smartphone using face recognition or verifying identity at airport security.
Use Cases: Security, access control, and personalized services.
Learning meaningful representations from raw image data.
Example: Extracting features that capture edges, textures, or high-level concepts.
Use Cases: Transfer learning, dimensionality reduction, and unsupervised learning.
Assigning labels or categories to images based on their visual content.
Example: Identifying whether an image contains “people,” “animals,” “outdoor scenes,” or specific types of food.
Use Cases: Content organization, search, and recommendation systems.
Creating new images based on learned patterns or combining existing images.
Example: Generating realistic faces, creating art, or enhancing low-resolution images.
Use Cases: Artistic expression, data augmentation, and content creation.
Detecting and localizing specific objects within an image.
Example: Identifying and outlining the location of cars, pedestrians, or other objects in a street scene.
Use Cases: Autonomous vehicles, surveillance, and robotics.
Continuously tracking the movement of specific objects across frames in a video or image sequence.
Example: Following the trajectory of a moving vehicle or a person.
Use Cases: Surveillance, sports analysis, and augmented reality.
Assigning a category label to each pixel in an image.
Example: Distinguishing between different parts of an image (e.g., road, sky, trees) in a scene.
Use Cases: Medical imaging, scene understanding, and image editing.
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