Computer Vision and Speech

CV and Speech From Recognition to Understanding Modern AI often starts with recognition: spotting objects in images or transcribing speech. Yet practical systems must move beyond recognizing signals to understanding their meaning and intent. This shift in computer vision and speech helps machines explain what to do next and supports human decision making. It is a gradual path from raw labels to useful conclusions. From recognition to understanding Recognition answers what is there. Understanding adds why it matters and what actions to take. Context, history, and clear goals make the difference. Temporal patterns reveal actions, while multimodal signals—combining sight and sound—reduce ambiguity. With understanding, a system can propose next steps, not just identify a scene. ...

Computer Vision and Speech Processing Today Two big areas shape how machines perceive the world: vision and audio. Today, advances in computer vision and speech processing fit into everyday products and critical systems. The goal is simple to describe but hard to achieve: machines should understand what we see and hear, then respond in useful ways. Current Trends Key drivers include more powerful GPUs, diverse data, and new learning methods. Self-supervised learning reduces labeling needs. Transformer models from NLP have spread to vision and audio, enabling flexible representations that work across tasks. Edge devices are finally strong enough to run high quality models with low latency. ...