A Multimodal Depth-Aware Method For Embodied Reference Understanding

• URL: http://arxiv.org/abs/2510.08278v2
• Date: Fri, 10 Oct 2025 13:05:00 GMT
• Title: A Multimodal Depth-Aware Method For Embodied Reference Understanding
• Authors: Fevziye Irem Eyiokur, Dogucan Yaman, Hazım Kemal Ekenel, Alexander Waibel,
• Abstract summary: Embodied Reference Understanding requires identifying a target object in a visual scene based on both language instructions and pointing cues.<n>We propose a novel ERU framework that jointly leverages data augmentation, depth-map modality, and a depth-aware decision module.
• Score: 56.30142869506262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied Reference Understanding requires identifying a target object in a visual scene based on both language instructions and pointing cues. While prior works have shown progress in open-vocabulary object detection, they often fail in ambiguous scenarios where multiple candidate objects exist in the scene. To address these challenges, we propose a novel ERU framework that jointly leverages LLM-based data augmentation, depth-map modality, and a depth-aware decision module. This design enables robust integration of linguistic and embodied cues, improving disambiguation in complex or cluttered environments. Experimental results on two datasets demonstrate that our approach significantly outperforms existing baselines, achieving more accurate and reliable referent detection.

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