GACR reformulates cloud removal as an observation-anchored residual inversion process with geo-contextual prior alignment to preserve semantic structures for improved downstream interpretation tasks.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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2026 3representative citing papers
SpectraDINO extends DINOv2 with lightweight per-modality adapters and staged distillation to handle NIR, SWIR, and LWIR in one backbone, but its SWIR gain is weakened by using the evaluation dataset for pretraining.
MonoIR-RS synthesizes 600K infrared remote-sensing images from visible sources, rewrites captions to be IR-aware, and shows that IR-aware fine-tuning improves CLIP retrieval by up to 12.8 points and drives VLM infrared-cue coverage to 100% with near-zero RGB-color leakage.
citing papers explorer
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Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment
GACR reformulates cloud removal as an observation-anchored residual inversion process with geo-contextual prior alignment to preserve semantic structures for improved downstream interpretation tasks.
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SpectraDINO: Modality-Conditioned Adaptation of RGB Vision Foundation Models Across Infrared Bands
SpectraDINO extends DINOv2 with lightweight per-modality adapters and staged distillation to handle NIR, SWIR, and LWIR in one backbone, but its SWIR gain is weakened by using the evaluation dataset for pretraining.
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MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
MonoIR-RS synthesizes 600K infrared remote-sensing images from visible sources, rewrites captions to be IR-aware, and shows that IR-aware fine-tuning improves CLIP retrieval by up to 12.8 points and drives VLM infrared-cue coverage to 100% with near-zero RGB-color leakage.