Multi-Modal Computer Vision and Foundation Models In Agriculture
in conjunction with IEEE CVPR 2025

Overview

The integration of computer vision and deep learning into agriculture has gained significant traction, with advancements in multi-modal computer vision foundation models driving innovation in agricultural applications. This tutorial will introduce attendees to the latest developments, focusing on multi-modal sensor fusion, domain adaptation, and the application of foundation models in agricultural tasks.

The tutorial features leading experts who will provide insights into cutting-edge research and practical implementations. Through presentations and discussions, attendees will gain knowledge about the latest AI trends in multi-modal computer vision and foundation models applied to agriculture.

Invited Speakers 

Organizers

Schedule and Outline

Half-Day Tutorial – CVPR 2025

• 8:30 AM – Opening Remarks & Introductions

• 8:45 AM – 9:45 AM: Dr. Melba Crawford
  Multi-modal sensor fusion with Computer Vision models for multi-temporal yield prediction

  • ABSTRACT: TBD

• 9:45 AM – 10:00 AM: Coffee Break & Networking

• 10:00 AM – 11:00 AM: Dr. Soumik Sarkar
  Multi-modal Foundational Models in Agriculture

  • ABSTRACT: TBD

• 11:00 AM – 12:00 PM: Dr. Alex Schwing
  Recent Advances in Video View Synthesis, Video Object Segmentation, and Instance Segmentation

  • ABSTRACT: TBD

Attendees' Takeaways

By the end of this session, attendees will:
✅ Gain an understanding of multi-modal sensor fusion techniques for yield prediction.
✅ Learn about foundation models used for identifying pests, weeds, and crop health analysis.
✅ Explore recent advancements in video object segmentation and instance segmentation applied to agriculture.
✅ Connect with leading researchers and practitioners in the field of agricultural AI.

Target Audience 

This tutorial is designed for researchers, engineers, and practitioners in computer vision, AI, and agriculture. It aims to bridge the gap between cutting-edge AI research and real-world agricultural challenges, attracting professionals and students interested in applying AI to high-impact agricultural problems.

Diversity & Inclusion

This tutorial prioritizes diversity in expertise, gender, and background, bringing together experts from academia and industry to promote inclusivity in AI and agriculture.

Sponsors