Search Results for author:
Found 9 papers, 4 papers with code
Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects
Unlabeled 3D objects present an opportunity to leverage pretrained vision language models (VLMs) on a range of annotation tasks -- from describing object semantics to physical properties.
Constellation: Learning relational abstractions over objects for compositional imagination
Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning.
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Leveraging the shared structure that exists across different scenes, our model learns to infer two sets of latent representations from RGB video input alone: a set of "object" latents, corresponding to the time-invariant, object-level contents of the scene, as well as a set of "frame" latents, corresponding to global time-varying elements such as viewpoint.
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order across time, and predicts how those objects evolve over successive frames.
PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization
We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner.
AlignNet: Unsupervised Entity Alignment
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision.
Multi-Object Representation Learning with Iterative Variational Inference
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.
MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence.
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity.
