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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2504.11705 |
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| _version_ | 1866908729205587968 |
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| author | D'Alessandro, Adriano Mahdavi-Amiri, Ali Hamarneh, Ghassan |
| author_facet | D'Alessandro, Adriano Mahdavi-Amiri, Ali Hamarneh, Ghassan |
| contents | Class-agnostic counting (CAC) methods reduce annotation costs by letting users define what to count at test-time through text or visual exemplars. However, current open-vocabulary approaches work well for broad categories but fail when fine-grained category distinctions are needed, such as telling apart waterfowl species or pepper cultivars. We present FiGO, a new annotation-free method that adapts existing counting models to fine-grained categories using only the category name. Our approach uses a text-to-image diffusion model to create synthetic examples and a joint positive/hard-negative loss to learn a compact concept embedding that conditions a specialization module to convert outputs from any frozen counter into accurate, fine-grained estimates. To evaluate fine-grained counting, we introduce LOOKALIKES, a dataset of 37 subcategories across 14 parent categories with many visually similar objects per image. Our method substantially outperforms strong open-vocabulary baselines, moving counting systems from "count all the peppers" to "count only the habaneros." |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FiGO: Fine-Grained Object Counting without Annotations D'Alessandro, Adriano Mahdavi-Amiri, Ali Hamarneh, Ghassan Computer Vision and Pattern Recognition Class-agnostic counting (CAC) methods reduce annotation costs by letting users define what to count at test-time through text or visual exemplars. However, current open-vocabulary approaches work well for broad categories but fail when fine-grained category distinctions are needed, such as telling apart waterfowl species or pepper cultivars. We present FiGO, a new annotation-free method that adapts existing counting models to fine-grained categories using only the category name. Our approach uses a text-to-image diffusion model to create synthetic examples and a joint positive/hard-negative loss to learn a compact concept embedding that conditions a specialization module to convert outputs from any frozen counter into accurate, fine-grained estimates. To evaluate fine-grained counting, we introduce LOOKALIKES, a dataset of 37 subcategories across 14 parent categories with many visually similar objects per image. Our method substantially outperforms strong open-vocabulary baselines, moving counting systems from "count all the peppers" to "count only the habaneros." |
| title | FiGO: Fine-Grained Object Counting without Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.11705 |