Fetching the paper…
Reading the bibliography…
Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
Greedy learning of multiple objects in images using robust statistics and factorial learning
Christopher KI Williams and Michalis K Titsias · 2004
Earlier work this paper cites.
Clustering on the unit hypersphere using von mises-fisher distributions
Arindam Banerjee, Inderjit S Dhillon, Joydeep Ghosh, Suvrit Sra, and Greg Ridgeway · 2005
Earlier work this paper cites.
Pictorial structures for object recognition
Pedro F Felzenszwalb and Daniel P Huttenlocher · 2005
Earlier work this paper cites.
k-means++: The advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Cascade object detection with deformable part models
Pedro F Felzenszwalb, Ross B Girshick, and David McAllester · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Object detection with grammar models
Ross Girshick, Pedro Felzenszwalb, and David McAllester · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
Beyond pascal: A benchmark for 3d object detection in the wild
Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese · 2014
Earlier work this paper cites.
Amodal instance segmentation
Ke Li and Jitendra Malik · 2016
Earlier work this paper cites.
Overcoming occlusion with inverse graphics
Pol Moreno, Christopher KI Williams, Charlie Nash, and Pushmeet Kohli · 2016
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Earlier work this paper cites.
Simple does it: Weakly supervised instance and semantic segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, and Bernt Schiele · 2017
Cited alongside, same era.
Model-based image analysis for forensic shoe print recognition
Adam Kortylewski · 2017
Cited alongside, same era.
Vision-as-inverse-graphics: Obtaining a rich 3d explanation of a scene from a single image
Lukasz Romaszko, Christopher KI Williams, Pol Moreno, and Pushmeet Kohli · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Semantic amodal segmentation
Yan Zhu, Yuandong Tian, Dimitris Metaxas, and Piotr Dollar · 2017
Cited alongside, same era.
Occlusion-aware 3d morphable models and an illumination prior for face image analysis
Bernhard Egger, Sandro Schönborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer, and Thomas Vetter · 2018
Where are the masks: Instance segmentation with image-level supervision
Issam H Laradji, David Vazquez, and Mark Schmidt · 2019
Later among the works it cites.
Amodal instance segmentation with kins dataset
Lu Qi, Li Jiang, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
Later among the works it cites.
Robustness of object recognition under extreme occlusion in humans and computational models
Hongru Zhu, Peng Tang, Jeongho Park, Soojin Park, and Alan Yuille · 2019
Later among the works it cites.
Learning instance activation maps for weakly supervised instance segmentation
Yi Zhu, Yanzhao Zhou, Huijuan Xu, Qixiang Ye, David Doermann, and Jianbin Jiao · 2019
Later among the works it cites.
Weakly supervised instance segmentation by learning annotation consistent instances
Aditya Arun, CV Jawahar, and M Pawan Kumar · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The importance of amodal completion in everyday perception
Bence Nanay · 2018
Cited alongside, same era.
Weakly supervised instance segmentation using class peak response
Yanzhao Zhou, Yi Zhu, Qixiang Ye, Qiang Qiu, and Jianbin Jiao · 2018
Cited alongside, same era.
Weakly supervised learning of instance segmentation with inter-pixel relations
Jiwoon Ahn, Sunghyun Cho, and Suha Kwak · 2019
Cited alongside, same era.
Object counting and instance segmentation with image-level supervision
Hisham Cholakkal, Guolei Sun, Fahad Shahbaz Khan, and Ling Shao · 2019
Cited alongside, same era.
Learning to see the invisible: End-to-end trainable amodal instance segmentation
Patrick Follmann, Rebecca König, Philipp Härtinger, Michael Klostermann, and Tobias Böttger · 2019
Cited alongside, same era.
Learning to see the invisible: End-to-end trainable amodal instance segmentation
Patrick Follmann, Rebecca Kö Nig, Philipp Hä Rtinger, Michael Klostermann, and Tobias Bö Ttger · 2019
Cited alongside, same era.
Compositional convolutional neural networks: A deep architecture with innate robustness to partial occlusion
Adam Kortylewski, Ju He, Qing Liu, and Alan L Yuille · 2020
Closest in time.
Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion
Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, and Alan Yuille · 2020
Closest in time.
Combining compositional models and deep networks for robust object classification under occlusion
Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, and Alan Yuille · 2020
Closest in time.
Variational amodal object completion
Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, and Sanja Fidler · 2020
Closest in time.
Robust object detection under occlusion with context-aware compositionalnets
Angtian Wang, Yihong Sun, Adam Kortylewski, and Alan L Yuille · 2020
Closest in time.
Amodal segmentation based on visible region segmentation and shape prior
Yuting Xiao, Yanyu Xu, Ziming Zhong, Weixin Luo, Jiawei Li, and Shenghua Gao · 2020
Closest in time.
Self-supervised scene de-occlusion
Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, and Chen Change Loy · 2020
Closest in time.
A weakly supervised amodal segmenter with boundary uncertainty estimation
Khoi Nguyen and Sinisa Todorovic · 2021
Closest in time.
Nemo: Neural mesh models of contrastive features for robust 3d pose estimation
Angtian Wang, Adam Kortylewski, and Alan Yuille · 2021
Closest in time.