MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
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 infra
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Why these links exist
- Linked via arxiv authorJiaju Han →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorMa Yaqi →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorYahui Chai →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorXuemeng Sun →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorXin Lin →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorQike Zhang →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorYingying Zhao →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorXiang Chen →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorLuwei Yang →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorChengyin Hu →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
- Linked via arxiv authorJiahuan Long →
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation