Multimodal Vision Research Laboratory

MVRL

About Us

We are a computer vision lab at Washington University in St. Louis, led by Nathan Jacobs. We develop representation learning, geospatial AI, and generative modeling methods for satellite, aerial, and ground imagery, with extensions to audio, text, and structured data. We validate these methods on real-world challenges in biodiversity and conservation, agriculture, environmental monitoring, the built environment, and transportation safety.

Spotlight Publications

  1. Dhakal A, Khanal S, Sastry S, Arndt J, Dias PA, Lunga D, Jacobs N. 2026. SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  2. Sastry S, Khanal S, Dhakal A, Lin J, Cher D, Jarosz P, Jacobs N. 2026. ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  3. Muhawenayo G, Robinson C, Khanal S, Fang Z, Corley I, Wollam A, Gao T, Strnad L, Avery R, Estes L, Tárano AM, Jacobs N, Kerner H. 2026. PRUE: A Practical Recipe for Field Boundary Segmentation at Scale. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  4. Sarkar A, Sastry S, Pirinen A, Jacobs N, Vorobeychik Y. 2026. DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments. In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
  5. Thumbnail for VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics
    Cher D, Wei B, Sastry S, Jacobs N. 2026. VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
  6. Thumbnail for Global and Local Entailment Learning for Natural World Imagery
    Sastry S, Dhakal A, Xing E, Khanal S, Jacobs N. 2025. Global and Local Entailment Learning for Natural World Imagery. In: IEEE/CVF International Conference on Computer Vision (ICCV).
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