B-MoE: A Body-Part-Aware Mixture-of-Experts "All Parts Matter" Approach to Micro-Action Recognition
Fuente:
arXiv
Saved in:
| Main Authors: | Poddar, Nishit, Reka, Aglind, Borza, Diana-Laura, Majhi, Snehashis, Balazia, Michal, Das, Abhijit, Bremond, Francois |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Introducing Gating and Context into Temporal Action Detection
by: Reka, Aglind, et al.
Published: (2024)
by: Reka, Aglind, et al.
Published: (2024)
What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon
by: Tiwari, Utkarsh, et al.
Published: (2024)
by: Tiwari, Utkarsh, et al.
Published: (2024)
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
by: D'Amicantonio, Giacomo, et al.
Published: (2025)
by: D'Amicantonio, Giacomo, et al.
Published: (2025)
Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network
by: Sinha, Sanya, et al.
Published: (2025)
by: Sinha, Sanya, et al.
Published: (2025)
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets
by: Agrawal, Tanay, et al.
Published: (2025)
by: Agrawal, Tanay, et al.
Published: (2025)
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis
by: Reddy, Shukesh, et al.
Published: (2024)
by: Reddy, Shukesh, et al.
Published: (2024)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)
by: Polson, Nick, et al.
Published: (2026)
$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)
by: Takashiro, Shota, et al.
Published: (2026)
One-for-All Does Not Work! Enhancing Vulnerability Detection by Mixture-of-Experts (MoE)
by: Yang, Xu, et al.
Published: (2025)
by: Yang, Xu, et al.
Published: (2025)
MH-MoE: Multi-Head Mixture-of-Experts
by: Huang, Shaohan, et al.
Published: (2024)
by: Huang, Shaohan, et al.
Published: (2024)
MoE-Loco: Mixture of Experts for Multitask Locomotion
by: Huang, Runhan, et al.
Published: (2025)
by: Huang, Runhan, et al.
Published: (2025)
Are Visual-Language Models Effective in Action Recognition? A Comparative Study
by: Ali, Mahmoud, et al.
Published: (2024)
by: Ali, Mahmoud, et al.
Published: (2024)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
by: Pióro, Maciej, et al.
Published: (2024)
by: Pióro, Maciej, et al.
Published: (2024)
MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)
by: Chen, Xiaodong, et al.
Published: (2025)
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
by: Ludziejewski, Jan, et al.
Published: (2025)
by: Ludziejewski, Jan, et al.
Published: (2025)
MoE Lens -- An Expert Is All You Need
by: Chaudhari, Marmik, et al.
Published: (2026)
by: Chaudhari, Marmik, et al.
Published: (2026)
Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)
by: Gan, Wensheng, et al.
Published: (2025)
SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)
by: Kulibaba, Stepan, et al.
Published: (2026)
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
by: Xu, Yuhao, et al.
Published: (2026)
by: Xu, Yuhao, et al.
Published: (2026)
MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting
by: Jin, In-Hwan, et al.
Published: (2025)
by: Jin, In-Hwan, et al.
Published: (2025)
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
by: Feng, Jiarui, et al.
Published: (2026)
by: Feng, Jiarui, et al.
Published: (2026)
DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)
SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)
by: Muzio, Alexandre, et al.
Published: (2024)
Just Dance with $π$! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
by: Majhi, Snehashis, et al.
Published: (2025)
by: Majhi, Snehashis, et al.
Published: (2025)
AM Flow: Adapters for Temporal Processing in Action Recognition
by: Agrawal, Tanay, et al.
Published: (2024)
by: Agrawal, Tanay, et al.
Published: (2024)
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)
by: Su, Yang, et al.
Published: (2025)
Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)
by: Luo, Shuqing, et al.
Published: (2024)
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)
by: Sun, Weigao, et al.
Published: (2025)
Astro-MoE: Mixture of Experts for Multiband Astronomical Time Series
by: Cádiz-Leyton, Martina, et al.
Published: (2025)
by: Cádiz-Leyton, Martina, et al.
Published: (2025)
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)
by: Li, Yunxin, et al.
Published: (2024)
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
by: Tang, Yehui, et al.
Published: (2025)
by: Tang, Yehui, et al.
Published: (2025)
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
by: Liu, Yang, et al.
Published: (2026)
by: Liu, Yang, et al.
Published: (2026)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
AW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detection
by: Lin, Hongwei, et al.
Published: (2026)
by: Lin, Hongwei, et al.
Published: (2026)
Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation
by: Dutta, Tapas K., et al.
Published: (2025)
by: Dutta, Tapas K., et al.
Published: (2025)
Critical process parameters optimization for hyperthermostable Bamylase production by Bacillus subtilis DJ5 using response surface methodology
by: Abhijit Poddar
Published: (2014)
by: Abhijit Poddar
Published: (2014)
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
by: Zhang, Boyang, et al.
Published: (2025)
by: Zhang, Boyang, et al.
Published: (2025)
Similar Items
-
Introducing Gating and Context into Temporal Action Detection
by: Reka, Aglind, et al.
Published: (2024) -
What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon
by: Tiwari, Utkarsh, et al.
Published: (2024) -
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
by: D'Amicantonio, Giacomo, et al.
Published: (2025) -
Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network
by: Sinha, Sanya, et al.
Published: (2025) -
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets
by: Agrawal, Tanay, et al.
Published: (2025)