A novel camera-RFID fusion framework with trajectory matching achieves reliable centimeter-level asset localization in forested environments even during temporary occlusions.
Rfid and camera fusion for recognition of human-object interactions,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
CLMM is a two-stage contrastive learning framework using CNN-DiffTransformer encoders and dual-branch fusion to improve multimodal human activity recognition under limited labels.
citing papers explorer
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Camera-RFID Fusion for Robust Asset Tracking in Forested Environments
A novel camera-RFID fusion framework with trajectory matching achieves reliable centimeter-level asset localization in forested environments even during temporary occlusions.
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Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data
CLMM is a two-stage contrastive learning framework using CNN-DiffTransformer encoders and dual-branch fusion to improve multimodal human activity recognition under limited labels.