Transfer-Learning

Image and Video Analysis with Deep Learning Image and video analysis use AI to interpret what we see. Deep learning models learn patterns from large data and can recognize objects, scenes, and actions. This makes it possible to build helpful search tools, safety checks, and smart cameras that adapt to real-world tasks. Core tasks include image classification, object detection, instance segmentation, pose estimation, video classification, and action recognition. For video, researchers combine spatial features with temporal information using 3D convolutions, recurrent nets, or transformers. The right approach depends on accuracy needs, latency, and the amount of labeled data available. ...

AI in Computer Vision and Multimodal Systems AI in computer vision has moved from simple labels to systems that understand scenes and reason across different inputs. Modern models read images, video, and other signals to support decisions in real time. This shift brings helpful assistants, safer automation, and better accessibility in many industries. Key capabilities today include object detection, segmentation, motion tracking, and scene understanding. Engineers often group these tasks into clear goals: what is in a frame, where is it, how it moves, and how confident we should be about the answer. Good data quality and robust training help these systems work in diverse conditions. ...