PAMF initializes flow matching with missingness-type priors and shares encoder weights between imputation and classification to improve multimodal time-series prediction under incomplete observations.
MAESTRO : Adaptive Sparse Attention and Robust Learning for Multimodal Dynamic Time Series
5 Pith papers cite this work. Polarity classification is still indexing.
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TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
HALo uses smartglasses IMU head orientation to localize conversation partners' acoustic zones, achieving 21% better performance with known partner count, while CoCo classifies partner numbers at 0.74 accuracy using only IMU data.
MuViS is a new unified benchmark showing that neither gradient-boosted trees nor deep neural networks hold a universal advantage in multimodal virtual sensing.
SentryFuse delivers modality-aware zero-shot pruning and sparse attention that improves accuracy by 12.7% on average and up to 18% under sensor dropout while cutting memory 28.2% and latency up to 1.63x across multimodal edge models.
citing papers explorer
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PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
PAMF initializes flow matching with missingness-type priors and shares encoder weights between imputation and classification to improve multimodal time-series prediction under incomplete observations.
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TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and sentiment benchmarks under missingness.
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Towards Localizing Conversation Partners using Head Motion
HALo uses smartglasses IMU head orientation to localize conversation partners' acoustic zones, achieving 21% better performance with known partner count, while CoCo classifies partner numbers at 0.74 accuracy using only IMU data.
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MuViS: Multimodal Virtual Sensing Benchmark
MuViS is a new unified benchmark showing that neither gradient-boosted trees nor deep neural networks hold a universal advantage in multimodal virtual sensing.
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Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference
SentryFuse delivers modality-aware zero-shot pruning and sparse attention that improves accuracy by 12.7% on average and up to 18% under sensor dropout while cutting memory 28.2% and latency up to 1.63x across multimodal edge models.