SkyVLaM: Multimodal Large Language Model for UAV Video Understanding in Remote Sensing
Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved remote sensing (RS) multimodal understanding. Language-conditioned segmentation is crucial for fine-grained target understanding in Unmanned Aerial Vehicle (UAV) videos. However, this task remains challenging due to the prevalence of small, visually ambiguous targets and dynamic aerial perspectives. In this paper, we propose SkyVLaM, a multimodal large language model for UAV video understanding. SkyVLaM constructs sparse tokens directly from patch-level video representations through a temporal basis perceiver, regularizes the sparse basis to encourage complementary temporal cues, and adaptively selects...
| Authors | Kaiwen Jing, Ruixu Jia, Bingyao Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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