Augmented commonsense knowledge for remote object grounding

Bahram Mohammadi, Yicong Hong, Yuankai Qi, Qi Wu, Shirui Pan, Javen Qinfeng Shi

Research output: Contribution to journalConference paperpeer-review

5 Citations (Scopus)

Abstract

The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as "Bring me the blue cushion in the master bedroom". To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation. Specifically, the proposed approach involves constructing a knowledge base by retrieving commonsense information from ConceptNet, followed by a refinement module to remove noisy and irrelevant knowledge. We further present ACK which consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment by integrating visible objects, commonsense knowledge, and concept history, which includes object and knowledge temporal information. Moreover, we add a new pipeline for the commonsensebased decision-making process which leads to more accurate local action prediction. Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark.

Original languageEnglish
Pages (from-to)4269-4277
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume38
Issue number5
DOIs
Publication statusPublished - 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024

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