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Combining the strengths of many existing predictors to obtain a Mixture of Experts which is superior to its individual components is an effective way to improve the performance without having to develop new architectures or train a model from scratch.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs · 1994
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An alternative model for mixtures of experts
Lei Xu, Michael Jordan, and Geoffrey E Hinton · 1994
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Twenty years of mixture of experts
Seniha Esen Yuksel, Joseph N Wilson, and Paul D Gader · 2012
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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R-FCN: Object detection via region-based fully convolutional networks
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Soft-nms – improving object detection with one line of code
Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S. Davis · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2017
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Cascade R-CNN: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Acquisition of localization confidence for accurate object detection
Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, and Yuning Jiang · 2018
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Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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Relaxed softmax: Efficient confidence auto-calibration for safe pedestrian detection
Lukás Neumann, Andrew Zisserman, and Andrea Vedaldi · 2018
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Localization recall precision (LRP): A new performance metric for object detection
Kemal Oksuz, Baris Can Cam, Emre Akbas, and Sinan Kalkan · 2018
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SparseR-CNN: End-to-end object detection with learnable proposals
Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, and Ping Luo · 2018
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Multimodal generative models for scalable weakly-supervised learning, 2018
Mike Wu and Noah Goodman · 2018
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Dota: A large-scale dataset for object detection in aerial images
Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang · 2018
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Yolact: Real-time instance segmentation
Daniel Bolya, Chong Zhou, Fanyi Xiao, and Yong Jae Lee · 2019
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MMDetection: Open mmlab detection toolbox and benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin · 2019
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Centernet: Keypoint triplets for object detection
Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian · 2019
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Lvis: A dataset for large vocabulary instance segmentation
Agrim Gupta, Piotr Dollar, and Ross Girshick · 2019
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Bounding box regression with uncertainty for accurate object detection
Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, and Xiangyu Zhang · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Mask scoring r-cnn
Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, and Xinggang Wang · 2019
Cited alongside, same era.
Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
Cited alongside, same era.
Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition, 2020
Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen · 2020
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Ace: Ally complementary experts for solving long-tailed recognition in one-shot, 2021
Jiarui Cai, Yizhou Wang, and Jenq-Neng Hwang · 2021
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Dynamic head: Unifying object detection heads with attentions
Xiyang Dai, Yinpeng Chen, Bin Xiao, Dongdong Chen, Mengchen Liu, Lu Yuan, and Lei Zhang · 2021
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Instances as queries
Yuxin Fang, Shusheng Yang, Xinggang Wang, Yu Li, Chen Fang, Ying Shan, Bin Feng, and Wenyu Liu · 2021
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Estimating and evaluating regression predictive uncertainty in deep object detectors
Ali Harakeh and Steven L. Waslander · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
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Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection
Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang · 2019
Cited alongside, same era.
Benchmarking robustness in object detection: Autonomous driving when winter is coming
C. Michaelis, B. Mitzkus, R. Geirhos, E. Rusak, O. Bringmann, A. S. Ecker, M. Bethge, and W. Brendel · 2019
Cited alongside, same era.
Measuring calibration in deep learning
Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
Cited alongside, same era.
Objects365: A large-scale, high-quality dataset for object detection
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun · 2019
Cited alongside, same era.
Variational mixture-of-experts autoencoders for multi-modal deep generative models, 2019
Yuge Shi, N. Siddharth, Brooks Paige, and Philip H. S. Torr · 2019
Cited alongside, same era.
Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
Cited alongside, same era.
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Rethinking calibration of deep neural networks: Do not be afraid of overconfidence
Deng-Bao Wang, Lei Feng, and Min-Ling Zhang · 2021
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Deformable {detr}: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2021
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Calibrating deep neural networks by pairwise constraints
Jiacheng Cheng and Nuno Vasconcelos · 2022
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Learning multimodal vaes through mutual supervision, 2022
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, and N. Siddharth · 2022
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Parametric and multivariate uncertainty calibration for regression and object detection
Fabian Kuppers, Jonas Schneider, and Anselm Haselhoff · 2022
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Rtmdet: An empirical study of designing real-time object detectors
Chengqi Lyu, Wenwei Zhang, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang, and Kai Chen · 2022
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Towards improving calibration in object detection under domain shift
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, and Mohsen Ali · 2022
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An impartial take to the cnn vs transformer robustness contest
Francesco Pinto, Philip H. S. Torr, and Puneet K. Dokania · 2022
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Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition
Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng · 2022
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Eva: Exploring the limits of masked visual representation learning at scale
Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, and Yue Cao · 2023
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Correlation loss: Enforcing correlation between classification and localization
Fehmi Kahraman, Kemal Oksuz, Sinan Kalkan, and Emre Akbas · 2023
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Large selective kernel network for remote sensing object detection
Yuxuan Li, Qibin Hou, Zhaohui Zheng, Mingming Cheng, Jian Yang, and Xiang Li · 2023
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
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Bridging precision and confidence: A train-time loss for calibrating object detection
Muhammad Akhtar Munir, Muhammad Haris Khan, Salman Khan, and Fahad Khan · 2023
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Towards building self-aware object detectors via reliable uncertainty quantification and calibration
Kemal Oksuz, Tom Joy, and Puneet K. Dokania · 2023
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Multiclass confidence and localization calibration for object detection
Bimsara Pathiraja, Malitha Gunawardhana, and Muhammad Haris Khan · 2023
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Multi-modal queried object detection in the wild
Yifan Xu, Mengdan Zhang, Chaoyou Fu, Peixian Chen, Xiaoshan Yang, Ke Li, and Changsheng Xu · 2023
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Detrs with collaborative hybrid assignments training
Zhuofan Zong, Guanglu Song, and Yu Liu · 2023
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