Foundational Few-Shot Object Detection Challenge [CVPR 2025]

Peter Robicheaux discusses the collaboration between Roboflow and Carnegie Mellon University for the second iteration of the Foundational Few-Shot Object Detection Challenge at CVPR 2025, introducing the Roboflow-20VL dataset. This dataset comprises 20 diverse datasets from novel domains such as supermarket product localization and defect detection, utilizing various imaging modalities including X-Rays and thermal images. Each dataset features a 10-shot split and aims to test the capability of foundation models to localize objects using a few visual and textual examples. The challenge addresses the limitations of existing models in recognizing rare classes and aims to inspire the development of robust algorithms that can learn with minimal examples. Participants are encouraged to submit methods to the EvalAI leaderboard, with top-performing teams to be recognized at the Workshop On Visual Perception and Learning in an Open World. The challenge runs from March 11 to June 8, 2025, and further details can be found through the Foundational FSOD Github issues page and previous research papers.