2020

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.

Occlusion resistant learning of intuitive physics from videos

Combines a compositional rendering network with a recurrent interaction network to learn dynamics in scenes with significant occlusion, but relies on ground-truth object positions and segmentations.

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

RELATE builds upon the interpretable, structured latent parameterization of BlockGAN by modeling the correlations between object parameters to generate realistic videos of dynamic scenes, using raw, unlabeled data.

Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases

Analysis of invariances in representations from contrastive self-supervised models reveals that they leverage aggressive cropping on object-centric datasets to improve occlusion invariance at the expense of viewpoint and category instance invariance.

Bootstrap Your Own Latent A New Approach to Self-Supervised Learning

BYOL improves on SotA self-supervised methods by introducing a target network, which removes the need for negative examples.