Indian Institute of Technology Bombay

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.

The work proposes COFLOW, an inference-time method that adaptively selects the step count for each generation based on prompt features and is trained online with an unsupervised reward balancing inference efficiency and generation fidelity; it is plug-and-play, requires no retraining of the underlying generative model, generalizes to image and video generation with over 2.5x speedup while preserving perceptual and semantic quality, and is accompanied by a theoretical analysis establishing an O(1/K) forward-Euler discretization error bound under standard regularity conditions.

The work proposes COFLOW, an inference-time method that adaptively selects the step count for each generation based on prompt features and is trained online with an unsupervised reward balancing inference efficiency and generation fidelity; it is plug-and-play, requires no retraining of the underlying generative model, generalizes to image and video generation with over 2.5x speedup while preserving perceptual and semantic quality, and is accompanied by a theoretical analysis establishing an O(1/K) forward-Euler discretization error bound under standard regularity conditions.