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Nathan Jacobs

Dr. Nathan Jacobs is a Professor in the Computer Science & Engineering department at Washington University in St. Louis and Director of the Multimodal Vision Research Laboratory. His research focuses on computer vision, specializing in learning-based algorithms for processing large-scale image collections. His current work develops techniques for understanding the visual world from geotagged imagery, including images from social networks, outdoor webcams, and satellites. His research has been funded by NSF, NIH, DARPA, IARPA, NGA, ARL, AFRL, and Google.

He has graduated 14 PhD students, with 6 placed in tenure-track faculty positions and others at leading technology companies including Microsoft, Zillow, and Kitware. He has also mentored numerous MS students and undergraduates, many of whom have gone on to pursue advanced degrees or careers in industry.

Research Lab  /  CV  /  Scholar  /  Github  /  LinkedIn

Roles and Affiliations

Selected Contributions

See my lab's page for a complete listing of publications.

  1. 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
  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
  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
  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
  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
  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
  7. Thumbnail for Towards Open-World Generation of Stereo Images and Unsupervised Matching
    Qiao F, Xiong Z, Xing E, Jacobs N. 2025. Towards Open-World Generation of Stereo Images and Unsupervised Matching. In: IEEE/CVF International Conference on Computer Vision (ICCV).
    code website paper bibtex
  8. 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).
    code website paper bibtex
  9. Thumbnail for ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
    Xing E, Kolouju P, Pless R, Stylianou A, Jacobs N. 2025. ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    code paper bibtex
  10. Thumbnail for RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings
    Dhakal A, Sastry S, Khanal S, Ahmad A, Xing E, Jacobs N. 2025. RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    code paper bibtex