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Found 25 papers, 16 papers with code
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots
Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets.
AudioSlots: A slot-centric generative model for audio separation
We discuss the results and limitations of our approach in detail, and further outline potential ways to overcome the limitations and directions for future work.
PaLM-E: An Embodied Multimodal Language Model
Large language models excel at a wide range of complex tasks.
Ranked #1 on
Visual Question Answering (VQA)
on OK-VQA
RUST: Latent Neural Scene Representations from Unposed Imagery
Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis.
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge.
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions.
Object Scene Representation Transformer
A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone.
Conditional Object-Centric Learning from Video
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.
On the Binding Problem in Artificial Neural Networks
Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences.
Learning Object-Centric Video Models by Contrasting Sets
We address this problem by introducing a global, set-based contrastive loss: instead of contrasting individual slot representations against one another, we aggregate the representations and contrast the joined sets against one another.
A Perspective on Objects and Systematic Generalization in Model-Based RL
In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment.
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.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world.
Neural Expectation Maximization
Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities.
Highway and Residual Networks learn Unrolled Iterative Estimation
We demonstrate that this viewpoint directly leads to the construction of Highway and Residual networks.
Tagger: Deep Unsupervised Perceptual Grouping
We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features.
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance.
Binding via Reconstruction Clustering
Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples.
Training Very Deep Networks
Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success.
Ranked #34 on
Image Classification
on MNIST
Highway Networks
There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success.
LSTM: A Search Space Odyssey
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.
A Clockwork RNN
Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs.
