CoLoRSMamba steers AudioMamba using video CLS-guided conditional LoRA to adapt selective state-space parameters, outperforming baselines on audio-filtered NTU-CCTV and DVD subsets with 88.63% and 75.77% accuracy respectively.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
verdicts
UNVERDICTED 4representative citing papers
ReTAMamba adds reliability decay modeling and chronological weaving to Mamba for irregular clinical time series and reports 7.5-10% relative AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.
citing papers explorer
-
CoLoRSMamba: Conditional LoRA-Steered Mamba for Supervised Multimodal Violence Detection
CoLoRSMamba steers AudioMamba using video CLS-guided conditional LoRA to adapt selective state-space parameters, outperforming baselines on audio-filtered NTU-CCTV and DVD subsets with 88.63% and 75.77% accuracy respectively.
-
ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
ReTAMamba adds reliability decay modeling and chronological weaving to Mamba for irregular clinical time series and reports 7.5-10% relative AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.
-
STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction
STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.
-
A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.