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Size-Informed Representations for Unsupervised Image Clustering
This paper investigates how image size and preprocessing impact unsupervised clustering, introducing a late-fusion size injection strategy that significantly improves performance on heterogeneous real-world datasets.
Philipp Rajah Moura Srivastava, Charilaos Apostolidis, Saumya Pailwan, Patrick Koller, Leandros Stefanopoulos
Cite DOI IEEE Xplore
Size-Informed Representations for Unsupervised Image Clustering