CLIP-Count: Towards Text-Guided Zero-Shot Object Counting

Understand

Recent advances in visual-language models have shown remarkable zero-shot text-image matching ability that is transferable to downstream tasks such as object detection and segmentation.

• Adapting these models for object counting, however, remains a formidable challenge.
• In this study, we first investigate transferring vision-language models (VLMs) for class-agnostic object counting.
• Specifically, we propose CLIP-Count, the first end-to-end pipeline that estimates density maps for open-vocabulary objects with text guidance in a zero-shot manner.