{"ID":6029825,"CreatedAt":"2026-07-08T02:57:47.77373338Z","UpdatedAt":"2026-07-10T17:41:27.792927618Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06552","arxiv_id":"2607.06552","title":"MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation","abstract":"Infrared remote-sensing imagery captures intensity structure, object-background contrast, and illumination-invariant cues often invisible in RGB imagery. Yet, most remote-sensing vision-language resources and models focus on visible-band semantics, leaving infrared vision-language understanding underexplored. We introduce MonoIR-RS, a large-scale infrared remote-sensing vision-language dataset and benchmark that couples IR-aware data construction with CLIP-style contrastive adaptation and VLM instruction tuning. Built from the same source pool and split as FusionRS, MonoIR-RS retains the infrared image as the model-facing modality, yielding 600,000 synthesized infrared images and 59,032 retained IR-aware caption records. The model experiments use this retained language-supervision subset, whose captions rewrite supervision around grayscale structure and infrared-style contrast instead of RGB appearance. We show that the synthesized infrared imagery is markedly closer to real thermal imagery than a grayscale conversion on the AVIID benchmark. We fine-tune five CLIP backbones and six VLM backbones, and calibrate them against zero-shot behavior: IR-aware adaptation lifts CLIP mean recall by up to 12.8 points and drives VLM captioning IR-cue coverage to 100% while reducing residual RGB-color leakage to near zero. By isolating the infrared modality from RGB-IR dual-modal learning, MonoIR-RS offers a controlled, reproducible testbed for aligning infrared remote-sensing evidence with language.","short_abstract":"Infrared remote-sensing imagery captures intensity structure, object-background contrast, and illumination-invariant cues often invisible in RGB imagery. Yet, most remote-sensing vision-language resources and models focus on visible-band semantics, leaving infrared vision-language understanding underexplored. We introd...","url_abs":"https://arxiv.org/abs/2607.06552","url_pdf":"https://arxiv.org/pdf/2607.06552v1","authors":"[\"Jiaju Han\",\"Ma Yaqi\",\"Yahui Chai\",\"Xuemeng Sun\",\"Xin Li\",\"Qike Zhang\",\"Yingying Zhao\",\"Xiang Chen\",\"Luwei Yang\",\"Chengyin Hu\",\"Jiahuan Long\"]","published":"2026-07-07T17:53:58Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}