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CoDi diffusion model advances low-shot object counting

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CoDi, a novel diffusion-based model for low-shot object counting that excels in dense regions with small objects. This model utilizes an exemplar-based conditioning module to extract and adjust object prototypes within the denoising network, leading to improved object location estimation. CoDi significantly outperforms existing state-of-the-art methods on the FSC benchmark across few-shot, one-shot, and reference-less scenarios, and also sets a new standard on the MCAC benchmark. AI

IMPACT This research could improve the accuracy of object detection and counting in complex visual scenes, benefiting applications in computer vision and image analysis.

RANK_REASON The cluster describes a new research paper detailing a novel model for object counting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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CoDi diffusion model advances low-shot object counting

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The cluster describes a new research paper detailing a novel model for object counting. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Grega \v{S}u\v{s}tar, Jer Pelhan, Alan Luke\v{z}i\v{c}, Matej Kristan ·

    CoDi -- an exemplar-conditioned diffusion model for low-shot counting

    arXiv:2512.20153v2 Announce Type: replace Abstract: Low-shot object counting addresses estimating the number of previously unobserved objects in an image using only few or no annotated test-time exemplars. A considerable challenge for modern low-shot counters are dense regions wi…

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