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New model 'Count Anything' tackles domain-specific object counting

Researchers have introduced "Count Anything," a generalist model designed for text-guided object counting across diverse domains. This model addresses the fragmentation in current object counting methods by unifying category-conditioned counting with spatial localization. It employs a dual-granularity approach, using both region-level and pixel-level counters to handle various object sizes and densities, and has been benchmarked on the newly constructed CLOC dataset. AI

IMPACT This model could unify and improve object counting across various fields, from medical imaging to remote sensing.

RANK_REASON The cluster describes a new research paper and model release detailing a novel approach to object counting.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Count Anything

    A generalist model for text-guided object counting across multiple domains is presented, utilizing dual-granularity instance enumeration and complementary counting fusion for improved accuracy and cross-domain generalization.

  2. arXiv cs.CV TIER_1 English(EN) · Mengqi Lei, Shuokun Cheng, Wei Bao, Shaoyi Du, Jun-Hai Yong, Siqi Li, Yue Gao ·

    Count Anything

    arXiv:2605.30846v1 Announce Type: new Abstract: Object counting remains fragmented across domain-specific datasets and task formulations, despite rapid progress in generalist vision models. Existing counting models are often tailored to scenarios such as crowds, vehicles, cells, …