Image de couverture de livre

MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Oksuz, Kemal, Kuzucu, Selim, Joy, Tom, Dokania, Puneet K.
Format:	Preprint
Publié:	2023
Sujets:	Computer Vision and Pattern Recognition
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines
par: Kuzucu, Selim, et autres
Publié: (2024)

Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges
par: Oksuz, Kemal, et autres
Publié: (2025)

Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation
par: Eiras, Francisco, et autres
Publié: (2023)

MObI: Multimodal Object Inpainting Using Diffusion Models
par: Buburuzan, Alexandru, et autres
Publié: (2025)

Placing Objects in Context via Inpainting for Out-of-distribution Segmentation
par: de Jorge, Pau, et autres
Publié: (2024)

Bucketed Ranking-based Losses for Efficient Training of Object Detectors
par: Yavuz, Feyza, et autres
Publié: (2024)

TrueMoE: Dual-Routing Mixture of Discriminative Experts for Synthetic Image Detection
par: Zhang, Laixin, et autres
Publié: (2025)

MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
par: Kong, Chenqi, et autres
Publié: (2024)

Class Uncertainty: A Measure to Mitigate Class Imbalance
par: Baltaci, Z. S., et autres
Publié: (2023)

YOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection
par: Meiraz, Ori, et autres
Publié: (2025)

AW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detection
par: Lin, Hongwei, et autres
Publié: (2026)

MoE-GS: Mixture of Experts for Dynamic Gaussian Splatting
par: Jin, In-Hwan, et autres
Publié: (2025)

Language-Unlocked ViT (LUViT): Empowering Self-Supervised Vision Transformers with LLMs
par: Kuzucu, Selim, et autres
Publié: (2025)

Fine-tuning can cripple your foundation model; preserving features may be the solution
par: Mukhoti, Jishnu, et autres
Publié: (2023)

DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification
par: Wang, Yuhao, et autres
Publié: (2024)

ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts
par: Han, Xumeng, et autres
Publié: (2024)

Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
par: Lei, Tianwu, et autres
Publié: (2024)

GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation
par: Yazıcı, Ziya Ata, et autres
Publié: (2024)

IMA-MoE: An Interpretable Modality-Aware Mixture-of-Experts Framework for Characterizing the Neurobiological Signatures of Binge Eating Disorder
par: Zhao, Lin, et autres
Publié: (2026)

Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models
par: Wang, Peiran, et autres
Publié: (2025)

MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
par: Lin, Bin, et autres
Publié: (2024)

Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
par: Vu, Nguyen Lan Vi, et autres
Publié: (2025)

GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts
par: Liao, Minwen, et autres
Publié: (2025)

CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts
par: Hackel, Leonard, et autres
Publié: (2025)

MoVA: Adapting Mixture of Vision Experts to Multimodal Context
par: Zong, Zhuofan, et autres
Publié: (2024)

LadderMoE: Ladder-Side Mixture of Experts Adapters for Bronze Inscription Recognition
par: Zhou, Rixin, et autres
Publié: (2025)

MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding
par: Chopra, Shivang, et autres
Publié: (2025)

MoE3D: A Mixture-of-Experts Module for 3D Reconstruction
par: Wang, Zichen, et autres
Publié: (2026)

R^2MoE: Redundancy-Removal Mixture of Experts for Lifelong Concept Learning
par: Guo, Xiaohan, et autres
Publié: (2025)

CBDES MoE: Hierarchically Decoupled Mixture-of-Experts for Functional Modules in Autonomous Driving
par: Xiang, Qi, et autres
Publié: (2025)

GazeMoE: Perception of Gaze Target with Mixture-of-Experts
par: Dai, Zhuangzhuang, et autres
Publié: (2026)

MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models
par: Ko, Dohwan, et autres
Publié: (2026)

MoE3D: Mixture of Experts meets Multi-Modal 3D Understanding
par: Li, Yu, et autres
Publié: (2025)

Mixture of Scale Experts for Alignment-free RGBT Video Object Detection and A Unified Benchmark
par: Wang, Qishun, et autres
Publié: (2024)

MoEE: Mixture of Emotion Experts for Audio-Driven Portrait Animation
par: Liu, Huaize, et autres
Publié: (2025)

CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
par: Li, Jiachen, et autres
Publié: (2024)

Domain-Specialized Object Detection via Model-Level Mixtures of Experts
par: Pavlitska, Svetlana, et autres
Publié: (2026)

WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal
par: Luo, Yulin, et autres
Publié: (2023)

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry
par: Le, Jiajun, et autres
Publié: (2025)

SkyMoE: A Vision-Language Foundation Model for Enhancing Geospatial Interpretation with Mixture of Experts
par: Liu, Jiaqi, et autres
Publié: (2025)