100 papers with code • 6 benchmarks • 16 datasets
Visual Navigation is the problem of navigating an agent, e.g. a mobile robot, in an environment using camera input only. The agent is given a target image (an image it will see from the target position), and its goal is to move from its current position to the target by applying a sequence of actions, based on the camera observations only.
LibrariesUse these libraries to find Visual Navigation models and implementations
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.
To this end, we propose a new federated learning algorithm that jointly learns compact local representations on each device and a global model across all devices.
As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in  to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average.
We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making.
To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine.
In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation.