Shi Z, Sun Y, Zhang M. Training-free object counting with prompts[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024: 323-331.
Abstract:
This paper tackles the problem of object counting in images. Existing approaches rely on extensive training data with point annotations for each object, making data collection labor-intensive and time-consuming. To overcome this, we propose a training-free object counter that treats the counting task as a segmentation problem. Our approach leverages the Segment Anything Model (SAM), known for its high-quality masks and zero-shot segmentation capability. However, the vanilla mask generation method of SAM lacks class-specific information in the masks, resulting in inferior counting accuracy. To overcome this limitation, we introduce a prior-guided mask generation method that incorporates three types of priors into the segmentation process, enhancing efficiency and accuracy. Additionally, we tackle the issue of counting objects specified through text by proposing a two-stage approach that combines reference object selection and prior-guided mask generation. Extensive experiments on standard datasets demonstrate the competitive performance of our training-free counter compared to learning-based approaches. This paper presents a promising solution for counting objects in various scenarios without the need for extensive data collection and counting-specific training. Code is available at https://github.com/shizenglin/training-free-object-counter.
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Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - AI Singapore Programme
Grant Reference no. : AISG2-RP-2021-025
This research / project is supported by the National Research Foundation, Singapore - National Research Foundation Fellowship Award
Grant Reference no. : NRF-NRFF15-2023-0001
This research / project is supported by the Early Career Investigatorship from Center for Frontier AI Research (CFAR), A*STAR - Startup grant
Grant Reference no. :
This research / project is supported by the Agency for Science, Technology and Research - Startup grant
Grant Reference no. :