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Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image.
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.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A benchmark dataset and evaluation methodology for video object segmentation
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Training imagenet in 3 hours for 25 minutes
Jeremy Howard · 2018
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Panoptic segmentation
Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollár · 2019
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Attention-guided unified network for panoptic segmentation
Yanwei Li, Xinze Chen, Zheng Zhu, Lingxi Xie, Guan Huang, Dalong Du, and Xingang Wang · 2019
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An end-to-end network for panoptic segmentation
Huanyu Liu, Chao Peng, Changqian Yu, Jingbo Wang, Xu Liu, Gang Yu, and Wei Jiang · 2019
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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
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Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 2019
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Rvos: End-to-end recurrent network for video object segmentation
Carles Ventura, Miriam Bellver, Andreu Girbau, Amaia Salvador, Ferran Marques, and Xavier Giro-i Nieto · 2019
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Mots: Multi-object tracking and segmentation
Paul Voigtlaender, Michael Krause, Aljosa Osep, Jonathon Luiten, Berin Balachandar Gnana Sekar, Andreas Geiger, and Bastian Leibe · 2019
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Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
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Upsnet: A unified panoptic segmentation network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, and Raquel Urtasun · 2019
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Video instance segmentation
Linjie Yang, Yuchen Fan, and Ning Xu · 2019
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Stem-seg: Spatio-temporal embeddings for instance segmentation in videos
Ali Athar, Sabarinath Mahadevan, Aljosa Osep, Laura Leal-Taixé, and Bastian Leibe · 2020
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Classifying, segmenting, and tracking object instances in video with mask propagation
Gedas Bertasius and Lorenzo Torresani · 2020
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Sipmask: Spatial information preservation for fast image and video instance segmentation
Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, and Ling Shao · 2020
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Scaling wide residual networks for panoptic segmentation
Liang-Chieh Chen, Huiyu Wang, and Siyuan Qiao · 2020
Cited alongside, same era.
Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation
Bowen Cheng, Maxwell D Collins, Yukun Zhu, Ting Liu, Thomas S Huang, Hartwig Adam, and Liang-Chieh Chen · 2020
Cited alongside, same era.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Bowen Cheng, Maxwell D Collins, Yukun Zhu, Ting Liu, Thomas S Huang, Hartwig Adam, and Liang-Chieh Chen · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Max-deeplab: End-to-end panoptic segmentation with mask transformers
Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen · 2021
Later among the works it cites.
A survey on deep learning technique for video segmentation
Wenguan Wang, Tianfei Zhou, Fatih Porikli, David Crandall, and Luc Van Gool · 2021
Later among the works it cites.
End-to-end video instance segmentation with transformers
Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, and Huaxia Xia · 2021
Later among the works it cites.
Step: Segmenting and tracking every pixel
Mark Weber, Jun Xie, Maxwell Collins, Yukun Zhu, Paul Voigtlaender, Hartwig Adam, Bradley Green, Andreas Geiger, Bastian Leibe, Daniel Cremers, et al · 2021
Later among the works it cites.
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Cited alongside, same era.
Video panoptic segmentation
Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon · 2020
Cited alongside, same era.
Mast: A memory-augmented self-supervised tracker
Zihang Lai, Erika Lu, and Weidi Xie · 2020
Cited alongside, same era.
Video instance segmentation tracking with a modified vae architecture
Chung-Ching Lin, Ying Hung, Rogerio Feris, and Linglin He · 2020
Cited alongside, same era.
Unovost: Unsupervised offline video object segmentation and tracking
Jonathon Luiten, Idil Esen Zulfikar, and Bastian Leibe · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Huiyu Wang, Yukun Zhu, Bradley Green, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen · 2020
Cited alongside, same era.
Segdiff: Image segmentation with diffusion probabilistic models
Tomer Amit, Eliya Nachmani, Tal Shaharbany, and Lior Wolf · 2021
Cited alongside, same era.
Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, and Philippe C Cattin · 2021
Later among the works it cites.
K-net: Towards unified image segmentation
Wenwei Zhang, Jiangmiao Pang, Kai Chen, and Chen Change Loy · 2021
Later among the works it cites.
Decoder denoising pretraining for semantic segmentation
Emmanuel Brempong Asiedu, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, and Mohammad Norouzi · 2022
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A unified sequence interface for vision tasks
Ting Chen, Saurabh Saxena, Lala Li, Tsung-Yi Lin, David J Fleet, and Geoffrey Hinton · 2022
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Analog bits: Generating discrete data using diffusion models with self-conditioning
Ting Chen, Ruixiang Zhang, and Geoffrey Hinton · 2022
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Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
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Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2022
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Diffusion adversarial representation learning for self-supervised vessel segmentation
Boah Kim, Yujin Oh, and Jong Chul Ye · 2022
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Tubeformer-deeplab: Video mask transformer
Dahun Kim, Jun Xie, Huiyu Wang, Siyuan Qiao, Qihang Yu, Hong-Seok Kim, Hartwig Adam, In So Kweon, and Liang-Chieh Chen · 2022
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Uvim: A unified modeling approach for vision with learned guiding codes
Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai, Jeremiah Harmsen, and Neil Houlsby · 2022
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Mask dino: Towards a unified transformer-based framework for object detection and segmentation
Feng Li, Hao Zhang, Shilong Liu, Lei Zhang, Lionel M Ni, Heung-Yeung Shum, et al · 2022
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Panoptic segformer: Delving deeper into panoptic segmentation with transformers
Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, Ping Luo, and Tong Lu · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Unified-io: A unified model for vision, language, and multi-modal tasks
Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris A Lee, Jonathan Ho, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2022
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Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang · 2022
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Cmt-deeplab: Clustering mask transformers for panoptic segmentation
Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen · 2022
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k-means mask transformer, 2022
Qihang Yu, Huiyu Wang, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hatwig Adam, Alan Yuille, and Liang-Chieh Chen · 2022
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Nested hierarchical transformer: Towards accurate, data-efficient and interpretable visual understanding
Zizhao Zhang, Han Zhang, Long Zhao, Ting Chen, Sercan Ö Arik, and Tomas Pfister · 2022
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