Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
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Abstract (arXiv)
In this paper, we investigate the applicability of the CLIP-EBC framework, originally designed for crowd counting, to car object counting using the CARPK dataset. Experimental results show that our model achieves second-best performance compared to existing methods. In addition, we propose a K-means weighted clustering method to estimate object positions based on predicted density maps, indicating the framework's potential extension to localization tasks.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 11 references that are in the map. First posted on arXiv 11 Jul 2025.
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References · 10
- 2017Attention Is All You Needcited by 60,361
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2022Visual Prompt Tuningcited by 1,681
- 2017Drone-based Object Counting by Spatially Regularized Regional Proposal Networkcited by 156
- 2022CounTR: Transformer-based Generalised Visual Countingcited by 79
- 2022Few-shot Object Counting with Similarity-Aware Feature Enhancementcited by 62
- 2024CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classificationcited by 12
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