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arXiv · 2306.01540

CLIPGraphs: Multimodal Graph Networks to Infer Object-Room Affinities

Ayush Agrawal·Raghav Arora·Ahana Datta·Snehasis Banerjee·Brojeshwar Bhowmick·Krishna Murthy Jatavallabhula·Mohan Sridharan·Madhava Krishna
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Abstract

This paper introduces a novel method for determining the best room to place an object in, for embodied scene rearrangement. While state-of-the-art approaches rely on large language models (LLMs) or reinforcement learned (RL) policies for this task, our approach, CLIPGraphs, efficiently combines commonsense domain knowledge, data-driven methods, and recent advances in multimodal learning. Specifically, it (a)encodes a knowledge graph of prior human preferences about the room location of different objects in home environments, (b) incorporates vision-language features to support multimodal queries based on images or text, and (c) uses a graph network to learn object-room affinities based on embeddings of the prior knowledge and the vision-language features. We demonstrate that our approach provides better estimates of the most appropriate location of objects from a benchmark set of object categories in comparison with state-of-the-art baselines

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Ayush Agrawal, Raghav Arora, Ahana Datta, Snehasis Banerjee, Brojeshwar Bhowmick, Krishna Murthy Jatavallabhula, Mohan Sridharan, Madhava Krishna. 2023-06-02. CLIPGraphs: Multimodal Graph Networks to Infer Object-Room Affinities. https://arxiv.org/abs/2306.01540

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