4 citations · 13 across the 9 of their papers we have counts for
10 papers
ParticleSplat: Self-supervised Object-centric Latent Particle Splatting
Lyuxing He, Daniel Guo, Elizabeth Terveen +3
We present ParticleSplat, a self-supervised object-centric learning method that decomposes scenes into a set of latent ''particles'' representing semantic entities through feedforw…
3D-DLP: Self-Supervised 3D Object-Centric Scene Representation Learning
Ellina Zhang, Madhaven Iyengar, Amir Zadeh +4
We introduce 3D-DLP, a self-supervised object-centric representation learning model that decomposes scene-level RGB-D or voxel observations into a set of 3D latent particles. Build…
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Tal Daniel, Carl Qi, Dan Haramati +5
We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM aut…
Hierarchical Entity-centric Reinforcement Learning with Factored Subgoal Diffusion
Dan Haramati, Carl Qi, Tal Daniel +3
We propose a hierarchical entity-centric framework for offline Goal-Conditioned Reinforcement Learning (GCRL) that combines subgoal decomposition with factored structure to solve l…
EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation
Carl Qi, Dan Haramati, Tal Daniel +2
Object manipulation is a common component of everyday tasks, but learning to manipulate objects from high-dimensional observations presents significant challenges. These challenges…
Entity-Centric Reinforcement Learning for Object Manipulation from Pixels
Dan Haramati, Tal Daniel, Aviv Tamar
Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to…
DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
Tal Daniel, Aviv Tamar
We propose a new object-centric video prediction algorithm based on the deep latent particle (DLP) representation. In comparison to existing slot- or patch-based representations, D…
Unsupervised Image Representation Learning with Deep Latent Particles
Tal Daniel, Aviv Tamar
We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into l…
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
Tal Daniel, Aviv Tamar
The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main…
Deep Variational Semi-Supervised Novelty Detection
Tal Daniel, Thanard Kurutach, Aviv Tamar
In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generat…