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New dataset Ampe-DB highlights generalization challenges in human activity recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Ampe-DB, a new multimodal dataset designed to capture human activity and interaction within the context of Ampe, a traditional Ghanaian game. The dataset includes RGB video, pose representations, and metadata from 15 participants, totaling 637 clips. Evaluations demonstrated that models trained on Ampe-DB generalize poorly to new participants and pairings, highlighting the challenges in recognizing fine-grained movements and interactions across different individuals. AI

IMPACT This dataset could advance research in fine-grained movement recognition and human-human interaction analysis, particularly concerning generalization across different individuals.

RANK_REASON The item describes a new dataset and its evaluation, which falls under research. [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 →

New dataset Ampe-DB highlights generalization challenges in human activity recognition

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The item describes a new dataset and its evaluation, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ruth Senior Biney, Isaac Kojo Yedonu Aboah ·

    Ampe-DB: A Dataset for Human Activity and Interaction Analysis Using the Ghanaian Game Ampe

    arXiv:2610.33130v1 Announce Type: new Abstract: Human activity recognition benchmarks underrepresent culturally situated forms of movement and interaction. We introduce Ampe-DB, a multimodal dataset based on Ampe, a traditional Ghanaian game involving coordinated jumping, leg mov…

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