4 citations · 7 across the 13 of their papers we have counts for
5 papers · 1 filter
ViPer: Visual Personalization of Generative Models via Individual Preference Learning
Sogand Salehi, Mahdi Shafiei, Teresa Yeo +2
Different users find different images generated for the same prompt desirable. This gives rise to personalized image generation which involves creating images aligned with an indiv…
Solving Vision Tasks with Simple Photoreceptors Instead of Cameras
Andrei Atanov, Jiawei Fu, Rishubh Singh +3
A de facto standard in solving computer vision problems is to use a common high-resolution camera and choose its placement on an agent (i.e., position and orientation) based on hum…
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi +6
Current multimodal and multitask foundation models like 4M or UnifiedIO show promising results, but in practice their out-of-the-box abilities to accept diverse inputs and perform…
BRAVE: Broadening the visual encoding of vision-language models
Oğuzhan Fatih Kar, Alessio Tonioni, Petra Poklukar +3
Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despi…
Controlled Training Data Generation with Diffusion Models
Teresa Yeo, Andrei Atanov, Harold Benoit +4
We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach and…