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Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

A self-adaptive visual navigation method (SAVN) which learns to adapt to new environments without any explicit supervision which shows major improvements in both success rate and SPL for visual navigation in novel scenes.
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Watching the World Go By: Representation Learning from Unlabeled Videos

Video Noise Contrastive Estimation is proposed, a method for using unlabeled video to learn strong, transferable single image representations that demonstrate improvements over recent unsupervised single image techniques, as well as over fully supervised ImageNet pretraining, across a variety of temporal and non-temporal tasks.
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SeGAN: Segmenting and Generating the Invisible

This paper studies the challenging problem of completing the appearance of occluded objects and proposes a novel solution, SeGAN, which outperforms state-of-the-art segmentation baselines for the invisible parts of objects.
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Contrasting Contrastive Self-Supervised Representation Learning Pipelines

This paper analyzes contrastive approaches as one of the most successful and popular variants of self-supervised representation learning and examines over 700 training experiments including 30 encoders, 4 pre-training datasets and 20 diverse downstream tasks.
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Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects

This paper addresses the problem of inferring contact points and the physical forces from videos of humans interacting with objects by using a physics simulator to predict effects, and enforce that estimated forces must lead to same effect as depicted in the video.
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ManipulaTHOR: A Framework for Visual Object Manipulation

This work proposes a framework for object manipulation built upon the physics-enabled, visually rich AI2-THOR framework and presents a new challenge to the Embodied AI community known as ArmPointNav, which extends the popular point navigation task to object manipulation and offers new challenges including 3D obstacle avoidance.
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Continuous Scene Representations for Embodied AI

Using CSR, state-of-the-art approaches for the challenging downstream task of visual room rearrangement are outperformed, without any task specific training and the learned embeddings capture salient spatial details of the scene and show applicability to real world data.
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Contrasting Contrastive Self-Supervised Representation Learning Models

This paper analyzes contrastive approaches as one of the most successful and popular variants of self-supervised representation learning and examines over 700 training experiments including 30 encoders, 4 pre-training datasets and 20 diverse downstream tasks.

Orientations of graphs avoiding given lists on out‐degrees

It is shown that if G is bipartite and |F (v)| ≤ dG(v) 2 for every v ∈ V (G), then G is F -avoiding.
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Who Let the Dogs Out? Modeling Dog Behavior from Visual Data

This model takes visual information as input and directly predicts the actions of the agent, and the representation learned by the model encodes distinct information compared to representations trained on image classification, and this learned representation can generalize to other domains.
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