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Found 15 papers, 12 papers with code
DeiSAM: Segment Anything with Deictic Prompting
To remedy this issue, we propose DeiSAM -- a combination of large pre-trained neural networks with differentiable logic reasoners -- for deictic promptable segmentation.
LEDITS++: Limitless Image Editing using Text-to-Image Models
Second, our methodology supports multiple simultaneous edits and is architecture-agnostic.
Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge
Text-conditioned image generation models have recently achieved astonishing image quality and alignment results.
Learning by Self-Explaining
Current AI research mainly treats explanations as a means for model inspection.
Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?
Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications.
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users.
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy.
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications.
SEGA: Instructing Text-to-Image Models using Semantic Guidance
This leaves the user with little semantic control.
AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
Generative transformer models have become increasingly complex, with large numbers of parameters and the ability to process multiple input modalities.
The Stable Artist: Steering Semantics in Diffusion Latent Space
Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone.
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models
Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications.
Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis
Models for text-to-image synthesis, such as DALL-E~2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public.
Does CLIP Know My Face?
Our large-scale experiments on CLIP demonstrate that individuals used for training can be identified with very high accuracy.
ILLUME: Rationalizing Vision-Language Models through Human Interactions
Bootstrapping from pre-trained language models has been proven to be an efficient approach for building vision-language models (VLM) for tasks such as image captioning or visual question answering.
