Aniket Junghare

Cross-modal remote sensing retrieval must match semantically identical scenes between synthetic aperture radar and multispectral imagery, yet earlier JEPA-style methods regress a single deterministic latent for each masked patch and assume every region is equally predictable. CAPE-JEPA instead outputs a per-patch Gaussian mean and variance, trains it by negative log-likelihood, and adds adversarial cross-modal distributional regularization plus in-training contrastive learning. On BEN-14K and fMoW it beats baselines including X-JEPA, SkySense and CROMA across all eight retrieval directions, gaining up to 13.74 points over X-JEPA on BEN-14K cross-modal directions and up to 15.59 on fMoW, with 407.30M parameters versus 432.72M.

Cross-modal remote sensing retrieval must match semantically identical scenes between synthetic aperture radar and multispectral imagery, yet earlier JEPA-style methods regress a single deterministic latent for each masked patch and assume every region is equally predictable. CAPE-JEPA instead outputs a per-patch Gaussian mean and variance, trains it by negative log-likelihood, and adds adversarial cross-modal distributional regularization plus in-training contrastive learning. On BEN-14K and fMoW it beats baselines including X-JEPA, SkySense and CROMA across all eight retrieval directions, gaining up to 13.74 points over X-JEPA on BEN-14K cross-modal directions and up to 15.59 on fMoW, with 407.30M parameters versus 432.72M.