CVPR 2026 Foundational Few-Shot Object Detection Challenge

Published: June 01, 2026

Challenge Summary

Can foundation models replace human annotators for 2D data annotation? Vision-Language Models (VLMs) like GroundingDINO have demonstrated remarkable zero-shot 2D detection performance on standard benchmarks like COCO. However, such foundational models may still be sub-optimal for specific target applications like medical and aerial image analysis Indeed, this well-known observation has created the ad-hoc practice of prompt engineering, where users actively search for a textual prompt that elicits the desired zero-shot behavior. Instead, we argue that one can principally address the challenge of aligning foundational models to target concepts through the lens of few-shot recognition by presenting VLMs with a few visual examples of the target concept. Crucially, such examples can be multi-modal, using both text and visual cues, mimicking the natural few-shot multi-modal instructions that are often given to human annotators when defining a target concept of interest. Concretely, we evaluate how well foundation models can learn from multi-modal annotator instructions using the Roboflow-VL dataset. We will evaluate detectors pre-trained on any external datasets and fine-tuned on multi-modal (text and visual) 10-shot examples per class. Methods will be evaluated on a held-out set of images and will be ranked by mean average precision (mAP).