HashSCD is a patch-wise hashing method for unsupervised scene change detection and localization that operates directly in Hamming space with competitive performance and lower computational cost.
Master’s thesis, Department of Computer Science, University of Toronto (2009)
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JEPAMatch augments FlexMatch with LeJEPA-derived latent regularization to produce better-structured representations, yielding higher accuracy and faster convergence on CIFAR-100, STL-10, and Tiny-ImageNet.
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From Image Hashing to Scene Change Detection
HashSCD is a patch-wise hashing method for unsupervised scene change detection and localization that operates directly in Hamming space with competitive performance and lower computational cost.
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JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
JEPAMatch augments FlexMatch with LeJEPA-derived latent regularization to produce better-structured representations, yielding higher accuracy and faster convergence on CIFAR-100, STL-10, and Tiny-ImageNet.