Artificial intelligence is no longer limited to single-model applications.
In many real-world scenarios, multiple models need to work together to deliver a seamless user experience. This is especially true for applications that process multi-modal data such as video, audio, and text. A single model usually handles only one type of task (e.g., object detection, speech recognition, or translation), but complex applications often require a pipeline of models that run either in sequence (serial execution) or in parallel.
Take the example of a video conferencing application:
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On the video stream, one model might first perform super-resolution to enhance image quality, followed by another model that performs face detection or recognition. In addition, many conferencing systems also support background replacement (virtual background), which typically requires a segmentation model to separate the person from the background before overlaying a new scene.
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On the audio stream, a model may remove background noise and echo and then pass the cleaned signal to a speech recognition model. The recognized text can then be fed into a machine translation model to support real-time multilingual communication.
