We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing.

For what purpose was the dataset created? We created this CityRefer dataset to facilitate research toward city-scale 3D visual grounding. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? What do the instances that comprise the dataset represent? CityRefer contains descriptions for 3D visual grounding on large-scale point cloud data.

In this paper, we introduce the CityRefer dataset for city-level 3D visual grounding.

We aim to evaluate Large Language Models (LLMs) for embodied decision making.

Though LLMs are capable of generating plausible programs, it's challenging to interact with the LLMs further to revise the program, especially if the user's specific requirements are different from the initial proposal.

Neural Information Processing Systems http://nips.cc/

However,most existing studies concentrate onthetext-to-image synthesis [11-14], which generates images from text descriptions without the original image.

Neural Information Processing Systems http://nips.cc/

We achieve an average task solve rate of 66% compared to a 29% solve rate by GPT -4's intrinsic planning with chain-of-thought prompting.