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Found 17 papers, 13 papers with code
Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View
These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn.
Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals
We illustrate Dramatron's usefulness as an interactive co-creative system with a user study of 15 theatre and film industry professionals.
CLIP-CLOP: CLIP-Guided Collage and Photomontage
The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art.
Collaborative Storytelling with Human Actors and AI Narrators
We introduced novel constraints on our language model to produce longer narrative text and tested the model in rehearsals with a team of professional improvisers.
Generative Art Using Neural Visual Grammars and Dual Encoders
Whilst there are perhaps only a few scientific methods, there seem to be almost as many artistic methods as there are artists.
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar.
Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View
These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn.
Ranked #7 on
Vision and Language Navigation
on Touchdown Dataset
Cross-View Policy Learning for Street Navigation
The ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL).
The StreetLearn Environment and Dataset
These datasets cannot be used for decision-making and reinforcement learning, however, and in general the perspective of navigation as an interactive learning task, where the actions and behaviours of a learning agent are learned simultaneously with the perception and planning, is relatively unsupported.
Learning To Follow Directions in Street View
Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision.
Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre
In this work, we have employed it to control what a subset of human actors say during an improv performance.
Learning to Navigate in Cities Without a Map
We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
Improvised Comedy as a Turing Test
The best improvisational theatre actors can make any scene partner, of any skill level or ability, appear talented and proficient in the art form, and thus "make them shine".
Learning to Navigate in Complex Environments
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.
Dependency Recurrent Neural Language Models for Sentence Completion
Recent work on language modelling has shifted focus from count-based models to neural models.
