AI RESEARCH
CountFormer: A Transformer Framework for Learning Visual Repetition and Structure in Class-Agnostic Object Counting
arXiv CS.CV
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ArXi:2510.23785v2 Announce Type: replace Humans can often count unfamiliar objects by observing visual repetition and composition, rather than relying only on object categories. However, many exemplar-free counting models struggle in such situations and may overcount when objects contain symmetric components, repeated substructures, or partial occlusion. We