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IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text

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arxiv 2210.14395 v1 pith:D4QI374D submitted 2022-10-26 cs.CV cs.CLcs.LG

Seungwhan Moon , Andrea Madotto , Zhaojiang Lin , Alireza Dirafzoon , Aparajita Saraf , Amy Bearman , Babak Damavandi This is my paper · ORCID
classification cs.CVcs.CLcs.LG
keywords imu2clipmotionapproachcontrastivepre-trainingsensorstextvideos
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We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with video and text, by projecting them into the joint representation space of Contrastive Language-Image Pre-training (CLIP). The proposed approach allows IMU2CLIP to translate human motions (as measured by IMU sensors) into their corresponding textual descriptions and videos -- while preserving the transitivity across these modalities. We explore several new IMU-based applications that IMU2CLIP enables, such as motion-based media retrieval and natural language reasoning tasks with motion data. In addition, we show that IMU2CLIP can significantly improve the downstream performance when fine-tuned for each application (e.g. activity recognition), demonstrating the universal usage of IMU2CLIP as a new pre-trained resource. Our code will be made publicly available.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 9 citations worldwide. Full citation record

  1. SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SImpHAR simulates bio-impedance signals from 3D motion and text, then uses contrastive pretraining and fine-tuning to improve impedance-based human activity recognition on two of three datasets.

  2. QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization

    cs.AI 2026-07 conditional novelty 5.0 of 10

    QLPO resamples GRPO training groups to favor short correct and long incorrect responses, cutting reasoning length substantially while keeping accuracy roughly unchanged.

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