FM-ChangeNet: Learning Change through Pathwise Feature Transport
We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field $\hat{v}_θ(z_t,t)$ along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only segmentation and enabling the model to capture temporal evolution explicitly. The learned velocity field is not only a trans...
| Authors | Roie Kazoom, George Leifman, Genady Beryozkin |
|---|---|
| Primary category | cs.AI |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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