74 citations · 226 across the 53 of their papers we have counts for
7 papers · 1 filter
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations
Maitreya Patel, Changhoon Kim, Sheng Cheng +2
Text-to-image (T2I) diffusion models, notably the unCLIP models (e.g., DALL-E-2), achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost o…
Adversarial Bayesian Augmentation for Single-Source Domain Generalization
Sheng Cheng, Tejas Gokhale, Yezhou Yang
Generalizing to unseen image domains is a challenging problem primarily due to the lack of diverse training data, inaccessible target data, and the large domain shift that may exis…
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
Changhoon Kim, Kyle Min, Maitreya Patel +2
The rapid advancement of generative models, facilitating the creation of hyper-realistic images from textual descriptions, has concurrently escalated critical societal concerns suc…
ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models
Maitreya Patel, Tejas Gokhale, Chitta Baral +1
The ability to understand visual concepts and replicate and compose these concepts from images is a central goal for computer vision. Recent advances in text-to-image (T2I) models…
End-to-end Knowledge Retrieval with Multi-modal Queries
Man Luo, Zhiyuan Fang, Tejas Gokhale +2
We investigate knowledge retrieval with multi-modal queries, i.e. queries containing information split across image and text inputs, a challenging task that differs from previous w…
Attributing Image Generative Models using Latent Fingerprints
Guangyu Nie, Changhoon Kim, Yezhou Yang +1
Generative models have enabled the creation of contents that are indistinguishable from those taken from nature. Open-source development of such models raised concerns about the ri…
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling
Ethan Wisdom, Tejas Gokhale, Chaowei Xiao +1
In this work, we present a data poisoning attack that confounds machine learning models without any manipulation of the image or label. This is achieved by simply leveraging the mo…