8 citations · 35 across the 20 of their papers we have counts for
7 papers · 1 filter
DECORAIT -- DECentralized Opt-in/out Registry for AI Training
Kar Balan, Alex Black, Simon Jenni +3
We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training as well as receive reward for their contribution…
MOFO: MOtion FOcused Self-Supervision for Video Understanding
Mona Ahmadian, Frank Guerin, Andrew Gilbert
Self-supervised learning (SSL) techniques have recently produced outstanding results in learning visual representations from unlabeled videos. Despite the importance of motion in s…
DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer
Dan Ruta, Gemma Canet Tarrés, Andrew Gilbert +3
Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image. Tra…
NeAT: Neural Artistic Tracing for Beautiful Style Transfer
Dan Ruta, Andrew Gilbert, John Collomosse +2
Style transfer is the task of reproducing the semantic contents of a source image in the artistic style of a second target image. In this paper, we present NeAT, a new state-of-the…
EKILA: Synthetic Media Provenance and Attribution for Generative Art
Kar Balan, Shruti Agarwal, Simon Jenni +3
We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visua…
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
Soon Yau Cheong, Armin Mustafa, Andrew Gilbert
Text-to-image models (T2I) such as StableDiffusion have been used to generate high quality images of people. However, due to the random nature of the generation process, the person…
ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer
Dan Ruta, Gemma Canet Tarres, Alexander Black +2
Representation learning aims to discover individual salient features of a domain in a compact and descriptive form that strongly identifies the unique characteristics of a given sa…