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Found 9 papers, 7 papers with code
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body.
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements.
LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On
In this context, image-based virtual try-on, which consists in generating a novel image of a target model wearing a given in-shop garment, has yet to capitalize on the potential of these powerful generative solutions.
Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing
Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner.
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Recent advancements in diffusion models have enabled the generation of realistic deepfakes by writing textual prompts in natural language.
Dress Code: High-Resolution Multi-Category Virtual Try-On
Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024x768) with front-view, full-body reference models.
Ranked #5 on
Virtual Try-on
on VITON
Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study
We developed a predictive 10-year type 2 diabetes risk score using 301 features derived from 472, 830 participants in the UK Biobank dataset while excluding any features which are not easily obtainable by a smartphone.
Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study
Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score.
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time.
