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Found 4 papers, 2 papers with code
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
Sequence and Circle: Exploring the Relationship Between Patches
The CRE considers the central patch as the center of the circle and measures the distance of the remaining patches from the center based on the four neighborhoods principle.
Contrastive Learning Through Time
We propose a family of CLTT algorithms based on state-of-the-art contrastive learning methods and test them on three data sets.
Self-supervised asymmetric deep hashing with margin-scalable constraint
In this paper, we propose a novel self-supervised asymmetric deep hashing method with a margin-scalable constraint(SADH) approach to cope with these problems.
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