Abstract Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encoder-based techniques have emerged as a new effective approach for T2I personalization, reducing the need for multiple images and long training times.
Designing machine learning architectures for processing neural networks in their raw weight matrix form is a newly introduced research direction. Unfortunately, the unique symmetry structure of deep weight spaces makes this design very challenging. …
Graph Neural Networks (GNN) are inherently limited in their expressive power. Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler-Lehman (WL) hierarchy as a measure of expressive power. Although this hierarchy has …
Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding. Surprisingly, however, there is still no clear understanding of the relation between these two …
We describe “Cascade”, a new counterfactual reasoning setup. An agent is provided a semantic instruction and the results of a played out dynamical system. Its goal is to intervene in the dynamic environment, triggering a cascade of events that will lead to a different and counterfactual outcome.
Abstract Point-cloud data collected in real-world applications are often incomplete, because objects are being observed from specific viewpoints, which only capture one perspective. Data can also be incomplete due to occlusion and low-resolution sampling.
Summary: We present Perfusion, a new text-to-image personalization method. With only a 100KB model size, trained for roughly 4 minutes, Perfusion can creatively portray personalized objects. It allows significant changes in their appearance, while maintaining their identity, using a novel mechanism we call “Key-Locking”.
Summary: We use an encoder to personalize a text-to-image model to new concepts with a single image and 5-15 tuning steps.
Abstract: Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language prompts.
Equivariance to permutations and rigid motions is an important inductive bias for various 3D learning problems. Recently it has been shown that the equivariant Tensor Field Network architecture is universal -- it can approximate any equivariant …
Many machine learning tasks involve processing eigenvectors derived from data. Especially valuable are Laplacian eigenvectors, which capture useful structural information about graphs and other geometric objects. However, ambiguities arise when …