Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks

Key points

• In this paper, we propose a multi-source and multi-dimensional semantic information fusion framework that coordinates terminal uploading and edge-side fusion under resource constraints.1
• We develop Hyper-Slot to combine semantic content with available token attributes through iterative token-to-slot assignment and slot-to-token feedback, producing a compact representation for global inference.1
• To encourage useful semantic uploading, we further design a contribution-based incentive mechanism that uses Hyper-Slot assignments to evaluate information coverage and cross-type complementarity.1
• Experiments on a tri-modal extension of COCO2014 and image-text retrieval on Flickr30K show that Hyper-Slot improves mAP by 3.66 percentage points and RSum by 15.9 points over the strongest evaluated baselines, respectively, while reducing the number of parameters by more than 80% compared with a naive concatenation-based fusion baseline.1

Sources (1)

• [1]Multi-Source and Multi-Dimensional Information Fusion in Resource-Constrained Edge Networks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 09:45 AM

  Multi-source semantic information fusion enables edge networks to support global inference using observations collected by distributed terminals.

  In this paper, we propose a multi-source and multi-dimensional semantic information fusion framework that coordinates terminal uploading and edge-side fusion under resource constraints.

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