Training data-efficient image transformers & distillation through attention

Produces competitive convolution-free transformer, training only on ImageNet.

Scaling Laws for Neural Language Models

A large-scale empirical invesigation of scaling laws shows that performance has a power-law relationship to model size, dataset size, and training compute, while architectural details have minimal effects.

Self-supervised learning through the eyes of a child

Applies self-supervised learning algorithms to developmentally realistic, longitudinal, egocentric video from young children and demonstrates the emergence of high-level visual representations.

Are we done with ImageNet?

Proposes a new set of ImageNet labels that address the limitations of the original labels resulting from multiple objects in a single image and synonymous labels.

Contrastive Learning of Structured World Models

Contrastively-trained Structured World Models (C-SWMs) depart from traditional pixel-based reconstruction losses and use an energy-based hinge loss for learning object-centric world models.

VisualCOMET: Reasoning about the Dynamic Context of a Still Image

By training on a large-scale repository of Visual Commonsense Graphs, VisualCOMET, a single stream vision-language transformer model, is able to generate inferences about past and present events by integrating information from the image with textual descriptions of the present event and location.

Forward Prediction for Physical Reasoning

Demonstrates the potential of forward-prediction for solving PHYRE physical reasoning tasks by investigating various combinations of object and pixel-based forward-prediction and task-solution models.

Hierarchical Relational Inference

Hierarchical Relational Inference (HRI) learns hierarchical object representations and their relations directly from raw visual inputs, but is evaluated against limited baselines on simple datasets

IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding

IntPhys provides a well-designed benchmark for evaluting a system's understanding of a few core concepts about the physics of objects.

Learning Long-term Visual Dynamics with Region Proposal Interaction Networks

Region Proposal Interaction Networks (RPIN) learn to reason about object trajectories in a latent region-proposal feature space, that captures object and contextual information.