Cover Image

AW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Hongwei, Huang, Xun, Wen, Chenglu, Wang, Cheng
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection
by: Huang, Xun, et al.
Published: (2024)

MoE3D: Mixture of Experts meets Multi-Modal 3D Understanding
by: Li, Yu, et al.
Published: (2025)

WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal
by: Luo, Yulin, et al.
Published: (2023)

L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection
by: Huang, Xun, et al.
Published: (2024)

Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
by: Wu, Hai, et al.
Published: (2024)

Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
by: Chen, Xuweiyi, et al.
Published: (2025)

MoE3D: A Mixture-of-Experts Module for 3D Reconstruction
by: Wang, Zichen, et al.
Published: (2026)

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
by: Lin, Bin, et al.
Published: (2024)

MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting
by: Jin, In-Hwan, et al.
Published: (2025)

MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
by: Kong, Chenqi, et al.
Published: (2024)

Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of Experts
by: Zhang, Yue, et al.
Published: (2025)

IMA-MoE: An Interpretable Modality-Aware Mixture-of-Experts Framework for Characterizing the Neurobiological Signatures of Binge Eating Disorder
by: Zhao, Lin, et al.
Published: (2026)

CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving
by: Xiang, Qi, et al.
Published: (2025)

SC3D: Label-Efficient Outdoor 3D Object Detection via Single Click Annotation
by: Xia, Qiming, et al.
Published: (2024)

Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
by: Lei, Tianwu, et al.
Published: (2024)

Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
by: Xia, Qiming, et al.
Published: (2025)

BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
by: Ma, Yingjie, et al.
Published: (2024)

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
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)

GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts
by: Liao, Minwen, et al.
Published: (2025)

CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
by: Zhang, Jihai, et al.
Published: (2024)

V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection
by: Huang, Xun, et al.
Published: (2024)

DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification
by: Wang, Yuhao, et al.
Published: (2024)

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs
by: Bo, Zi-Hao, et al.
Published: (2026)

SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts
by: Zhao, Shijia, et al.
Published: (2025)

TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts
by: Xu, Yu, et al.
Published: (2026)

eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking
by: Chen, Yucheng, et al.
Published: (2024)

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning
by: Guo, Xiaohan, et al.
Published: (2025)

B-MoE: A Body-Part-Aware Mixture-of-Experts "All Parts Matter" Approach to Micro-Action Recognition
by: Poddar, Nishit, et al.
Published: (2026)

V2VLoc: Robust GNSS-Free Collaborative Perception via LiDAR Localization
by: Lin, Wenkai, et al.
Published: (2025)

MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection
by: Oksuz, Kemal, et al.
Published: (2023)

Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
by: Li, Yunxin, et al.
Published: (2024)

SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation
by: Wang, Guoan, et al.
Published: (2024)

MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models
by: Ko, Dohwan, et al.
Published: (2026)

WinMamba: Multi-Scale Shifted Windows in State Space Model for 3D Object Detection
by: Zheng, Longhui, et al.
Published: (2025)

Diffusion-Based Restoration for Multi-Modal 3D Object Detection in Adverse Weather
by: He, Zhijian, et al.
Published: (2025)

M$^4$-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection
by: Liu, Jiyuan, et al.
Published: (2026)

3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow
by: Ma, Yueen, et al.
Published: (2025)

Seg2Box: 3D Object Detection by Point-Wise Semantics Supervision
by: Zheng, Maoji, et al.
Published: (2025)

MiM-DiT: MoE in MoE with Diffusion Transformers for All-in-One Image Restoration
by: Kong, Lingshun, et al.
Published: (2026)