Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

29 October 2023

Papers citing "Out-of-distribution Object Detection through Bayesian Uncertainty Estimation"

24 / 24 papers shown

Title
PViT: Prior-augmented Vision Transformer for Out-of-distribution Detection Tianhao Zhang Zhixiang Chen Lyudmila Mihaylova 238 0 0 27 Oct 2024
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions Wenhai Wang Jifeng Dai Zhe Chen Zhenhang Huang Zhiqi Li ... Tong Lu Lewei Lu Hongsheng Li Xiaogang Wang Yu Qiao VLM 104 680 0 10 Nov 2022
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors Ali Harakeh Steven L. Waslander UQCV 68 41 0 13 Jan 2021
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation Golnaz Ghiasi Huayu Chen A. Srinivas Rui Qian Nayeon Lee E. D. Cubuk Quoc V. Le Barret Zoph ISeg 286 987 0 13 Dec 2020
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving Di Feng Ali Harakeh Steven Waslander Klaus C. J. Dietmayer AAML UQCV EDL 65 225 0 20 Nov 2020
Energy-based Out-of-distribution Detection Weitang Liu Xiaoyun Wang John Douglas Owens Yixuan Li OODD 253 1,349 0 08 Oct 2020
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder Zhisheng Xiao Qing Yan Y. Amit OODD 126 193 0 06 Mar 2020
Input complexity and out-of-distribution detection with likelihood-based generative models Joan Serrà David Álvarez Vicencc Gómez Olga Slizovskaia José F. Núñez Jordi Luque OODD 126 276 0 25 Sep 2019
Likelihood Ratios for Out-of-Distribution Detection Jie Jessie Ren Peter J. Liu Emily Fertig Jasper Snoek Ryan Poplin M. DePristo Joshua V. Dillon Balaji Lakshminarayanan OODD 164 720 0 07 Jun 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 82 806 0 07 Feb 2019
Bounding Box Regression with Uncertainty for Accurate Object Detection Yihui He Chenchen Zhu Jianren Wang Marios Savvides Xinming Zhang ObjD 74 467 0 23 Sep 2018
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift Xiang Li Shuo Chen Xiaolin Hu Jian Yang 69 309 0 16 Jan 2018
Decoupled Weight Decay Regularization I. Loshchilov Frank Hutter OffRL 126 2,132 0 14 Nov 2017
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt Matthew D. Hoffman David M. Blei BDL 52 598 0 13 Apr 2017
Efficient Processing of Deep Neural Networks: A Tutorial and Survey Vivienne Sze Yu-hsin Chen Tien-Ju Yang J. Emer AAML 3DV 113 3,013 0 27 Mar 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 332 4,700 0 15 Mar 2017
Statistical Inference for Model Parameters in Stochastic Gradient Descent Xi Chen Jason D. Lee Xin T. Tong Yichen Zhang 56 138 0 27 Oct 2016
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 288 8,091 0 13 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 312 7,971 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 330 10,172 0 16 Mar 2016
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 202 1,510 0 08 Jun 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 419 43,234 0 11 Feb 2015
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 104 908 0 17 Feb 2014
Describing Textures in the Wild Mircea Cimpoi Subhransu Maji Iasonas Kokkinos S. Mohamed Andrea Vedaldi 3DV 96 2,661 0 14 Nov 2013