Search Results for author:
Found 6 papers, 3 papers with code
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models
In this paper, we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set.
VASE: Object-Centric Appearance and Shape Manipulation of Real Videos
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models.
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose PAIR Diffusion, a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in the image.
Spatial Entropy as an Inductive Bias for Vision Transformers
In this work, we propose a different and complementary direction, in which a local bias is introduced using an auxiliary self-supervised task, performed jointly with standard supervised training.
