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Found 5 papers, 5 papers with code
Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions
Current navigation studies concentrate on egocentric local descriptions (e. g., `it will be on your right') that require reasoning over the agent's local perception.
Apollo: Zero-shot MultiModal Reasoning with Multiple Experts
On the well-known task of stylized image captioning, our experiments show that our approach outperforms semi-supervised state-of-the-art models, while being zero-shot and avoiding costly training, data collection, and prompt engineering.
HeGeL: A Novel Dataset for Geo-Location from Hebrew Text
The task of textual geolocation - retrieving the coordinates of a place based on a free-form language description - calls for not only grounding but also natural language understanding and geospatial reasoning.
ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization
Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions.
RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation
Following navigation instructions in natural language requires a composition of language, action, and knowledge of the environment.
