Our framework is built upon diffusion models, leveraging the principle of guided sampling that incorporates an arbitrary energy-based guidance during inference time. The key defining feature of our sampler lies in its self-contained nature, i.e ., implementable solely with a pretrained model. This...
extended the application of diffusion models along the constant velocity path of ODE to achieve a more favorable balance between sampling steps and fidelity. The performance of our methods has been outstanding, outperforming related baseline comparisons in large-resolution datasets, such as ImageNet256,...
Subsequently, generalized (binary) linear mixed models (GLMM) will be used to assess the intervention’s effect on the dichotomous secondary outcomes. To identify predictors of successful resilience training, (generalized) linear mixed models will be used. A multivariable GLMM to identify potential pre...
In this paper, we proposed a self-supervised learning approach for precise facial expression recognition. Our approach leverages recent advancements in diffusion models, specifically the Classification and Regression Diffusion (CARD) model. To enhance the discriminative capability of our model, we ...
Massively parallel multiview stereopsis by surface normal diffusion. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 7–13 December 2015; pp. 873–881. [Google Scholar] Li, Z.; Wang, K.; Meng, D.; Xu, C. Multi-view stereo via depth map fusion:...
takes advantage of droplet-on-demand (DoD) inkjet printing to exploit interfacial fluid forces and guide molecular self-assembly into aligned or disordered nanofibers, hydrogel structures of different geometries and sizes, surface topographies and higher- ordered constructs bound by molecular diffusion. PA...