Paper · On the benchmark
Physics-inspired attention, measured against real gaze
MFA models visual attention as a magnetic field where image features act as sources generating attraction forces. Unlike CNN-based saliency models, MFA uses inverse-square field dynamics inspired by physics, requiring no training data or neural network weights. The model is evaluated on the MIT/Tübingen Saliency Benchmark (MIT300 leaderboard) with AUC 0.7926.
The result that matters: a model with zero learned parameters lands within reach of trained networks. That is evidence the magnetic-field idea is capturing something real about how gaze is drawn, not just curve-fitting — the practical side of the theory described in the companion paper.