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Found 10 papers, 3 papers with code
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks.
Topological Planning with Transformers for Vision-and-Language Navigation
Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments.
Task-GAN for Improved GAN based Image Restoration
Deep Learning (DL) algorithms based on Generative Adversarial Network (GAN) have demonstrated great potentials in computer vision tasks such as image restoration.
A Behavioral Approach to Visual Navigation with Graph Localization Networks
Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps.
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation.
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes.
Lattice Long Short-Term Memory for Human Action Recognition
This method effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity.
DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes
We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid.
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
Ranked #4 on
3D Reconstruction
on Data3D−R2N2
PMCE: efficient inference of expressive models of cancer evolution with high prognostic power
Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse.
