Multimodal Vision Research Laboratory

MVRL

Research Focus Areas

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Multimodal Representation Learning Research

Multimodal Representation Learning

Self-supervised and multimodal embeddings that align vision, language, and audio for robust learning across sensors and domains.

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Biodiversity and Conservation Research

Biodiversity and Conservation

Multimodal models for species, habitats, and environmental change—from natural-world imagery to conservation-ready monitoring.

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Geospatial AI Research

Geospatial AI

Planet-scale geospatial ML: unified embeddings, cross-view localization, and structured reasoning over satellite, aerial, and ground imagery.

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Generative Multimodal AI Research

Generative Multimodal AI

Generative models for images, 3D scenes, and multimodal earth data across scales and sensors.

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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. Thumbnail for UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving
    Xiong Z, Ye X, Yaman B, Cheng S, Lu Y, Luo J, Jacobs N, Ren L. 2026. UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving. In: European Conference on Computer Vision (ECCV).
    website paper bibtex GenAI Vision-Language Transportation
  2. Thumbnail for ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology
    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).
    code website paper press bibtex Ecology Representation Learning
  3. Thumbnail for SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
    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).
    code paper bibtex Representation Learning GenAI
  4. Thumbnail for PRUE: A Practical Recipe for Field Boundary Segmentation at Scale
    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).
    paper press bibtex GeoAI Agriculture
  5. Thumbnail for DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
    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).
    paper bibtex Reinforcement Learning GeoAI GenAI
  6. 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).
    paper bibtex GenAI GeoAI Representation Learning
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