Cognitively-Inspired Emergent Communication via Knowledge Graphs for Assisting the Visually Impaired
Ruxiao Chen, Dezheng Han, Wenjie Han, Shuaishuai Guo

TL;DR
This paper introduces VAG-EC, a cognitively-inspired emergent communication framework using knowledge graphs to improve real-time assistive navigation for the visually impaired, balancing speed and semantic richness.
Contribution
It presents a novel knowledge graph-based emergent communication method that emulates human cognition, enhancing interpretability and context-awareness in assistive systems.
Findings
VAG-EC outperforms traditional methods in TopSim and CI metrics.
The framework effectively balances latency and semantic depth.
Experiments show improved interpretability and adaptability.
Abstract
Assistive systems for visually impaired individuals must deliver rapid, interpretable, and adaptive feedback to facilitate real-time navigation. Current approaches face a trade-off between latency and semantic richness: natural language-based systems provide detailed guidance but are too slow for dynamic scenarios, while emergent communication frameworks offer low-latency symbolic languages but lack semantic depth, limiting their utility in tactile modalities like vibration. To address these limitations, we introduce a novel framework, Cognitively-Inspired Emergent Communication via Knowledge Graphs (VAG-EC), which emulates human visual perception and cognitive mapping. Our method constructs knowledge graphs to represent objects and their relationships, incorporating attention mechanisms to prioritize task-relevant entities, thereby mirroring human selective attention. This structured…
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Taxonomy
TopicsTactile and Sensory Interactions · Multimodal Machine Learning Applications · Social Robot Interaction and HRI
