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Found 10 papers, 4 papers with code
OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav
We present a single neural network architecture composed of task-agnostic components (ViTs, convolutions, and LSTMs) that achieves state-of-art results on both the ImageNav ("go to location in <this picture>") and ObjectNav ("find a chair") tasks without any task-specific modules like object detection, segmentation, mapping, or planning modules.
ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings
We present a scalable approach for learning open-world object-goal navigation (ObjectNav) -- the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e. g., "find a sink").
Offline Visual Representation Learning for Embodied Navigation
In this paper, we show that an alternative 2-stage strategy is far more effective: (1) offline pretraining of visual representations with self-supervised learning (SSL) using large-scale pre-rendered images of indoor environments (Omnidata), and (2) online finetuning of visuomotor representations on specific tasks with image augmentations under long learning schedules.
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Natural language instructions for visual navigation often use scene descriptions (e. g., "bedroom") and object references (e. g., "green chairs") to provide a breadcrumb trail to a goal location.
Sim-to-Real Transfer for Vision-and-Language Navigation
We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions.
Extended Abstract: Improving Vision-and-Language Navigation with Image-Text Pairs from the Web
Following a navigation instruction such as 'Walk down the stairs and stop near the sofa' requires an agent to ground scene elements referenced via language (e. g.'stairs') to visual content in the environment (pixels corresponding to 'stairs').
Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments – Extended Abstract
We develop a language-guided navigation task set in a continuous 3D environment where agents must execute low-level actions to follow natural language navigation directions.
Improving Vision-and-Language Navigation with Image-Text Pairs from the Web
Following a navigation instruction such as 'Walk down the stairs and stop at the brown sofa' requires embodied AI agents to ground scene elements referenced via language (e. g. 'stairs') to visual content in the environment (pixels corresponding to 'stairs').
Ranked #6 on
Vision and Language Navigation
on VLN Challenge
Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments
We develop a language-guided navigation task set in a continuous 3D environment where agents must execute low-level actions to follow natural language navigation directions.
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
Recently, modular networks have been shown to be an effective framework for performing visual reasoning tasks.
Ranked #4 on
Visual Question Answering (VQA)
on CLEVR
