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GeoSET introduces the first generalist foundation model for SAR-to-EO image translation, pretrained on over 3 million SAR–EO pairs and adaptable to downstream datasets via LoRA (0.60% parameter updates), achieving state-of-the-art FID and DISTS across six benchmarks.
ReFlowSET is a latent flow-matching framework for high-fidelity SAR-to-EO image translation, using codec selection and conditional DiT with vision model alignment to achieve state-of-the-art performance.
VaaWIT is an end-to-end framework enhancing Large Vision-Language Models for multilingual Web image translation via dual-stream attention and visual-aware adapters, outperforming SOTA baselines.
MNAFT (Modality Neuron-Aware Fine-Tuning) is a novel approach that selectively updates language-specific and language-agnostic neurons in multimodal large language models to improve image translation while preserving pre-trained knowledge. The method outperforms state-of-the-art image translation techniques including cascaded models and standard fine-tuning approaches.