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CountFormer: A Transformer Framework for Learning Visual Repetition and Structure in Class-Agnostic Object Counting

arXiv — cs.CV•Wednesday, October 29, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
Researchers have introduced CountFormer, a groundbreaking transformer framework designed to enhance the way machines count objects by focusing on visual repetition and structural relationships instead of just class identity. This innovation is significant because it addresses the limitations of current counting models, which often struggle with complex shapes and overlapping components. By improving object counting accuracy, CountFormer could have wide-ranging applications in fields like robotics and computer vision, making it easier for machines to understand and interact with the world.
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