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Redundancy-Free View Alignment for Multimodal Human Activity Recognition with Arbitrarily Missing Views

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-58621-8_45openalex-snapshot

Contrastive Multiview Coding

inferredcites →https://doi.org/10.1007/s11263-019-01217-wopenalex-snapshot

Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction

inferredcites →https://doi.org/10.1016/j.cviu.2023.103655openalex-snapshot

Global–local contrastive multiview representation learning for skeleton-based action recognition

inferredcites →https://doi.org/10.1016/j.inffus.2021.12.003openalex-snapshot

Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions

inferredcites →https://doi.org/10.1016/j.patcog.2010.04.019openalex-snapshot

Comparative study on classifying human activities with miniature inertial and magnetic sensors

inferredcites →https://doi.org/10.1109/access.2024.3480814openalex-snapshot

Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review

inferredcites →https://doi.org/10.1109/bigdata52589.2021.9671851openalex-snapshot

CONAN: Contrastive Fusion Networks for Multi-view Clustering

inferredcites →https://doi.org/10.1109/icdm.2018.00087openalex-snapshot

MuVAN: A Multi-view Attention Network for Multivariate Temporal Data

inferredcites →https://doi.org/10.1109/jsen.2024.3412397openalex-snapshot

Virtual Fusion With Contrastive Learning for Single-Sensor-Based Activity Recognition

inferredcites →https://doi.org/10.1109/taffc.2024.3378570openalex-snapshot

Contrastive Learning Based Modality-Invariant Feature Acquisition for Robust Multimodal Emotion Recognition With Missing Modalities

inferredcites →https://doi.org/10.1109/tkde.2025.3554028openalex-snapshot

A Survey on Mixture of Experts in Large Language Models

inferredcites →https://doi.org/10.1109/tmm.2024.3387298openalex-snapshot

Self-Weighted Contrastive Fusion for Deep Multi-View Clustering

inferredcites →https://doi.org/10.1145/3136755.3136817openalex-snapshot

Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks

inferredcites →https://doi.org/10.1145/3517246openalex-snapshot

ColloSSL

inferredcites →https://doi.org/10.1145/3539618.3591632openalex-snapshot

AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering

inferredcites →https://doi.org/10.1145/3550316openalex-snapshot

COCOA

inferredcites →https://doi.org/10.1609/aaai.v37i3.25378openalex-snapshot

Towards Good Practices for Missing Modality Robust Action Recognition

inferredcites →https://doi.org/10.1609/aaai.v39i21.34430openalex-snapshot

Mixture of Experts as Representation Learner for Deep Multi-View Clustering

inferredcites →https://doi.org/10.3390/s19173808openalex-snapshot

Multi-Sensor Fusion for Activity Recognition—A Survey

inferredcites →https://doi.org/10.52202/079017-2907openalex-snapshot

Your contrastive learning problem is secretly a distribution alignment problem

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