AMAR uses a transformer with learnable query embeddings for set-based prediction of concurrent activities from composite Wi-Fi CSI, combined with edge feature extraction and vector quantization for bandwidth-efficient deployment.
Momask: Generative masked modeling of 3d human motions
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
DAJI is a hierarchical framework using distillation and autoregressive generation to learn future-aware joint intents for language-conditioned humanoid robot control.
citing papers explorer
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AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI
AMAR uses a transformer with learnable query embeddings for set-based prediction of concurrent activities from composite Wi-Fi CSI, combined with edge feature extraction and vector quantization for bandwidth-efficient deployment.
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Before the Body Moves: Learning Anticipatory Joint Intent for Language-Conditioned Humanoid Control
DAJI is a hierarchical framework using distillation and autoregressive generation to learn future-aware joint intents for language-conditioned humanoid robot control.