CoDi diffusion model advances low-shot object counting

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 →

• arXiv
• CoDi
• DagsHub
• FSC benchmark
• Grega Šuštar
• Hugging Face
• MCAC benchmark

• paper
• model release

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