Tell me about the Hungarian algorithm. Where is it used in ML?

The Hungarian algorithm is an algorithm for solving the optimal matching problem in a bipartite graph, that is, finding the minimum or maximum weight perfect matching between two sets.

In machine learning and computer vision, it is often used for object matching tasks, such as object tracking or matching detections between frames.

Example: in multiple object tracking (MOT), it is necessary to match detections in the current frame with tracks from the previous frame. The Hungarian algorithm helps find the optimal match, minimizing the total distance (e.g., based on coordinates or features) between objects.

This allows for efficient solutions to data association problems, where optimality and accuracy of matching are important.