11 citations · 24 across the 13 of their papers we have counts for
4 papers · 1 filter
IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks
Jiarui Xu, Yossi Gandelsman, Amir Bar +4
In-context learning allows adapting a model to new tasks given a task description at test time. In this paper, we present IMProv - a generative model that is able to in-context lea…
Readout Guidance: Learning Control from Diffusion Features
Grace Luo, Trevor Darrell, Oliver Wang +2
We present Readout Guidance, a method for controlling text-to-image diffusion models with learned signals. Readout Guidance uses readout heads, lightweight networks trained to extr…
Sequential Modeling Enables Scalable Learning for Large Vision Models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam +5
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this, we define a common format…
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
Vidit Goel, Elia Peruzzo, Yifan Jiang +6
Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text, while others use low-level conditioning. Neverthel…