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Mamba-based model fuses RGB and skeleton data for action recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel cross-modal architecture for egocentric action recognition, integrating RGB video and hand skeleton data using a Mamba-based framework. This approach leverages the linear time complexity of State Space Models and introduces four Class (CLS) token mixing strategies for multimodal fusion. Experiments on the H2O dataset demonstrated that the 'Average' strategy significantly improved accuracy, outperforming the baseline by over 10% in the Tiny configuration and 2% in the Small configuration. AI

IMPACT Introduces a novel fusion strategy for multimodal action recognition, potentially improving performance in applications relying on egocentric video analysis.

RANK_REASON Academic paper detailing a new model architecture and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Mamba-based model fuses RGB and skeleton data for action recognition

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Academic paper detailing a new model architecture and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Juan Ignacio Bustos Gorostegui, Maria Elena Buemi ·

    Cross-Modal Action Recognition in Egocentric Video Using Mamba: Integrating RGB and Hand Skeleton Streams via CLS Token Fusion Strategies

    arXiv:2605.24302v1 Announce Type: new Abstract: Egocentric action recognition is a challenging task due to erratic camera motion, frequent hand occlusion, and the difficulty of maintaining consistent visual representations over time. In this work, we propose a cross-modal archite…

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