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. Existing approaches to completion rely on training models with datasets of predefined objects to guide the completion of point clouds. Unfortunately, these approaches fail to generalize when tested on objects or real-world setups that are poorly represented in their training set. Here, we leverage recent advances in text-guided 3D shape generation, showing how to use image priors for generating 3D objects. We describe an approach called SDS-Complete that uses a pre-trained text-to-image diffusion model and leverages the text semantics of a given incomplete point cloud of an object, to obtain a complete surface representation. SDS-Complete can complete a variety of objects using test-time optimization without expensive collection of 3D data. We evaluate SDS-Complete on a collection of incomplete scanned objects, captured by real-world depth sensors and LiDAR scanners. We find that it effectively reconstructs objects that are absent from common datasets, reducing Chamfer loss by about 50% on average compared with current methods.
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”. Perfusion can also combine individually learned concepts into a single generated image. Finally, it enables controlling the trade-off between visual and textual alignment at inference time, covering the entire Pareto front with just a single trained model.
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. However, current personalization approaches struggle with lengthy training times, high storage requirements or loss of identity. To overcome these limitations, we propose an encoder-based domain-tuning approach. Our key insight is that by underfitting on a large set of concepts from a given domain, we can improve generalization and create a model that is more amenable to quickly adding novel concepts from the same domain. Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e.g. a specific face, and learns to map it into a word-embedding representing the concept. Second, a set of regularized weight-offsets for the text-to-image model that learn how to effectively ingest additional concepts. Together, these components are used to guide the learning of unseen concepts, allowing us to personalize a model using only a single image and as few as 5 training steps - accelerating personalization from dozens of minutes to seconds, while preserving quality.
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 …
Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical properties are still …
Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their …