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.
In: ICLR (2015)
2 Pith papers cite this work. Polarity classification is still indexing.
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Semi-automated use of thresholded prior results as annotations enables training one deep network that outperforms human-in-the-loop TIL prediction across 12 cancer types.
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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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Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
Semi-automated use of thresholded prior results as annotations enables training one deep network that outperforms human-in-the-loop TIL prediction across 12 cancer types.