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Found 20 papers, 7 papers with code
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning.
Exploring through Random Curiosity with General Value Functions
Furthermore, RC-GVF significantly outperforms previous methods in the absence of ground-truth episodic counts in the partially observable MiniGrid environments.
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.
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers
The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems.
Test-time adaptation with slot-centric models
Current supervised visual detectors, though impressive within their training distribution, often fail to segment out-of-distribution scenes into their constituent entities.
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.
Unsupervised Object Keypoint Learning using Local Spatial Predictability
We propose PermaKey, a novel approach to representation learning based on object keypoints.
Hierarchical Relational Inference
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics.
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc.
Improving Generalization in Meta Reinforcement Learning using Learned Objectives
Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans.
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.
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
A disentangled representation encodes information about the salient factors of variation in the data independently.
A Case for Object Compositionality in Deep Generative Models of Images
In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.
FVD: A new Metric for Video Generation
While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.
Towards Accurate Generative Models of Video: A New Metric & Challenges
To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.
Investigating Object Compositionality in Generative Adversarial Networks
We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects.
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.
