MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection

26 September 2023

Papers citing "MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection"

9 / 9 papers shown

Title
A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications Siyuan Mu Sen Lin MoE 82 1 0 10 Mar 2025
Enhancing Domain Adaptation through Prompt Gradient Alignment Hoang Phan Lam C. Tran Quyen Tran Trung Le 47 0 0 13 Jun 2024
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection Muhammad Akhtar Munir Muhammad Haris Khan Salman Khan F. Khan UQCV 25 15 0 25 Mar 2023
Dense Distinct Query for End-to-End Object Detection Shilong Zhang Wang xinjiang Jiaqi Wang Jiangmiao Pang Chengqi Lyu Wenwei Zhang Ping Luo Kai-xiang Chen 64 111 0 22 Mar 2023
Correlation Loss: Enforcing Correlation between Classification and Localization Fehmi Kahraman Kemal Oksuz Sinan Kalkan Emre Akbas 23 4 0 03 Jan 2023
BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition Boyan Zhou Quan Cui Xiu-Shen Wei Zhao-Min Chen 240 765 0 05 Dec 2019
Feature Pyramid Networks for Object Detection Tsung-Yi Lin Piotr Dollár Ross B. Girshick Kaiming He Bharath Hariharan Serge J. Belongie ObjD 166 21,643 0 09 Dec 2016
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 261 10,106 0 16 Nov 2016