MoME: Estimating Psychological Traits from Gait with Multi-Stage Mixture of Movement Experts
Fuente:
arXiv
Saved in:
| Main Authors: | Cǎtrunǎ, Andy, Cosma, Adrian, Rǎdoi, Emilian |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition
by: Cosma, Adrian, et al.
Published: (2025)
by: Cosma, Adrian, et al.
Published: (2025)
The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models
by: Cătrună, Andy, et al.
Published: (2024)
by: Cătrună, Andy, et al.
Published: (2024)
GaitPT: Skeletons Are All You Need For Gait Recognition
by: Catruna, Andy, et al.
Published: (2023)
by: Catruna, Andy, et al.
Published: (2023)
CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild
by: Cătrună, Andy, et al.
Published: (2024)
by: Cătrună, Andy, et al.
Published: (2024)
Database-Agnostic Gait Enrollment using SetTransformers
by: Basoc, Nicoleta, et al.
Published: (2025)
by: Basoc, Nicoleta, et al.
Published: (2025)
Gait Recognition from Highly Compressed Videos
by: Niculae, Andrei, et al.
Published: (2024)
by: Niculae, Andrei, et al.
Published: (2024)
Aligning Actions and Walking to LLM-Generated Textual Descriptions
by: Chivereanu, Radu, et al.
Published: (2024)
by: Chivereanu, Radu, et al.
Published: (2024)
MoME: Mixture of Multimodal Experts for Cancer Survival Prediction
by: Xiong, Conghao, et al.
Published: (2024)
by: Xiong, Conghao, et al.
Published: (2024)
MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation
by: Rezvani, Arghavan, et al.
Published: (2025)
by: Rezvani, Arghavan, et al.
Published: (2025)
MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models
by: Shen, Leyang, et al.
Published: (2024)
by: Shen, Leyang, et al.
Published: (2024)
Spatial Colour Mixing Illusions as a Perception Stress Test for Vision-Language Models
by: Basoc, Nicoleta-Nina, et al.
Published: (2026)
by: Basoc, Nicoleta-Nina, et al.
Published: (2026)
MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition
by: Cappellazzo, Umberto, et al.
Published: (2025)
by: Cappellazzo, Umberto, et al.
Published: (2025)
LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
by: Kowsher, Md, et al.
Published: (2026)
by: Kowsher, Md, et al.
Published: (2026)
A Retrieval-Based Approach to Medical Procedure Matching in Romanian
by: Niculae, Andrei, et al.
Published: (2025)
by: Niculae, Andrei, et al.
Published: (2025)
What Makes a Good Doctor Response? A Study on Text-Based Telemedicine
by: Cosma, Adrian, et al.
Published: (2026)
by: Cosma, Adrian, 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)
MoVA: Adapting Mixture of Vision Experts to Multimodal Context
by: Zong, Zhuofan, et al.
Published: (2024)
by: Zong, Zhuofan, et al.
Published: (2024)
MoE3D: Mixture of Experts meets Multi-Modal 3D Understanding
by: Li, Yu, et al.
Published: (2025)
by: Li, Yu, et al.
Published: (2025)
DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification
by: Wang, Yuhao, et al.
Published: (2024)
by: Wang, Yuhao, et al.
Published: (2024)
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal
by: Luo, Yulin, et al.
Published: (2023)
by: Luo, Yulin, et al.
Published: (2023)
MoEE: Mixture of Emotion Experts for Audio-Driven Portrait Animation
by: Liu, Huaize, et al.
Published: (2025)
by: Liu, Huaize, et al.
Published: (2025)
ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts
by: Han, Xumeng, et al.
Published: (2024)
by: Han, Xumeng, et al.
Published: (2024)
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
by: Li, Jiachen, et al.
Published: (2024)
by: Li, Jiachen, et al.
Published: (2024)
MoCTEFuse: Illumination-Gated Mixture of Chiral Transformer Experts for Multi-Level Infrared and Visible Image Fusion
by: Jinfu, Li, et al.
Published: (2025)
by: Jinfu, Li, et al.
Published: (2025)
Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
by: Chen, Xuweiyi, et al.
Published: (2025)
by: Chen, Xuweiyi, et al.
Published: (2025)
Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
by: Wang, Peiran, et al.
Published: (2025)
by: Wang, Peiran, et al.
Published: (2025)
Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
by: Vu, Nguyen Lan Vi, et al.
Published: (2025)
by: Vu, Nguyen Lan Vi, et al.
Published: (2025)
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts
by: Liao, Minwen, et al.
Published: (2025)
by: Liao, Minwen, et al.
Published: (2025)
MoRE: 3D Visual Geometry Reconstruction Meets Mixture-of-Experts
by: Gao, Jingnan, et al.
Published: (2025)
by: Gao, Jingnan, et al.
Published: (2025)
SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation
by: Lu, Yujie, et al.
Published: (2026)
by: Lu, Yujie, et al.
Published: (2026)
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
by: Lin, Bin, et al.
Published: (2024)
by: Lin, Bin, et al.
Published: (2024)
MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection
by: Oksuz, Kemal, et al.
Published: (2023)
by: Oksuz, Kemal, et al.
Published: (2023)
PoseMoE: Mixture-of-Experts Network for Monocular 3D Human Pose Estimation
by: Liu, Mengyuan, et al.
Published: (2025)
by: Liu, Mengyuan, et al.
Published: (2025)
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
by: Bi, Hanbo, et al.
Published: (2025)
by: Bi, Hanbo, et al.
Published: (2025)
LadderMoE: Ladder-Side Mixture of Experts Adapters for Bronze Inscription Recognition
by: Zhou, Rixin, et al.
Published: (2025)
by: Zhou, Rixin, et al.
Published: (2025)
TrueMoE: Dual-Routing Mixture of Discriminative Experts for Synthetic Image Detection
by: Zhang, Laixin, et al.
Published: (2025)
by: Zhang, Laixin, et al.
Published: (2025)
MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding
by: Chopra, Shivang, et al.
Published: (2025)
by: Chopra, Shivang, et al.
Published: (2025)
R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning
by: Guo, Xiaohan, et al.
Published: (2025)
by: Guo, Xiaohan, et al.
Published: (2025)
MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models
by: Wang, Dianyi, et al.
Published: (2025)
by: Wang, Dianyi, et al.
Published: (2025)
CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving
by: Xiang, Qi, et al.
Published: (2025)
by: Xiang, Qi, et al.
Published: (2025)
Similar Items
-
On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition
by: Cosma, Adrian, et al.
Published: (2025) -
The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models
by: Cătrună, Andy, et al.
Published: (2024) -
GaitPT: Skeletons Are All You Need For Gait Recognition
by: Catruna, Andy, et al.
Published: (2023) -
CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild
by: Cătrună, Andy, et al.
Published: (2024) -
Database-Agnostic Gait Enrollment using SetTransformers
by: Basoc, Nicoleta, et al.
Published: (2025)