Learning What NOT to Count

• URL: http://arxiv.org/abs/2504.11705v1
• Date: Wed, 16 Apr 2025 02:05:47 GMT
• Title: Learning What NOT to Count
• Authors: Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh,
• Abstract summary: Few/zero-shot object counting methods often struggle to distinguish between fine-grained categories.<n>We propose an annotation-free approach that enables the seamless integration of new fine-grained categories into existing few/zero-shot counting models.<n>Our approach introduces an attention prediction network that identifies fine-grained category boundaries trained using only synthetic pseudo-annotated data.
• Score: 17.581015609730017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few/zero-shot object counting methods reduce the need for extensive annotations but often struggle to distinguish between fine-grained categories, especially when multiple similar objects appear in the same scene. To address this limitation, we propose an annotation-free approach that enables the seamless integration of new fine-grained categories into existing few/zero-shot counting models. By leveraging latent generative models, we synthesize high-quality, category-specific crowded scenes, providing a rich training source for adapting to new categories without manual labeling. Our approach introduces an attention prediction network that identifies fine-grained category boundaries trained using only synthetic pseudo-annotated data. At inference, these fine-grained attention estimates refine the output of existing few/zero-shot counting networks. To benchmark our method, we further introduce the FGTC dataset, a taxonomy-specific fine-grained object counting dataset for natural images. Our method substantially enhances pre-trained state-of-the-art models on fine-grained taxon counting tasks, while using only synthetic data. Code and data to be released upon acceptance.

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