8.6.1.2 Panoptic Segmentation

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Panoptic Segmentation. Perform instance segmentation for foreground instances and semantic segmentation for background simultaneously.
See also Instance Segmentation.

Chen, Q.[Qiang], Cheng, A.[Anda], He, X.Y.[Xiang-Yu], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
SpatialFlow: Bridging All Tasks for Panoptic Segmentation,
CirSysVideo(31), No. 6, June 2021, pp. 2288-2300.
IEEE DOI 2106
Task analysis, Image segmentation, Head, Object detection, Detectors, Semantics, Benchmark testing, Panoptic segmentation, location-aware BibRef

Gao, N.[Naiyu], Shan, Y.[Yanhu], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi],
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation,
IP(30), 2021, pp. 6013-6023.
IEEE DOI 2107
Semantic and instance together. Image segmentation, Semantics, Predictive models, Task analysis, Pipelines, Image color analysis, Head, Panoptic segmentation, pixel embedding BibRef

Chu, T.[Tao], Cai, W.J.[Wen-Jie], Liu, Q.[Qiong],
Learning panoptic segmentation through feature discriminability,
PR(122), 2022, pp. 108240.
Elsevier DOI 2112
Panoptic segmentation, Feature discriminability, Region refinement BibRef

de Carvalho, O.L.F.[Osmar Luiz Ferreira], de Carvalho Júnior, O.A.[Osmar Abílio], Rosa e Silva, C.[Cristiano], de Albuquerque, A.O.[Anesmar Olino], Santana, N.C.[Nickolas Castro], Borges, D.L.[Dibio Leandro], Gomes, R.A.T.[Roberto Arnaldo Trancoso], Guimarăes, R.F.[Renato Fontes],
Panoptic Segmentation Meets Remote Sensing,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, W.Q.[Wei-Qi], You, X.[Xiong], Yang, J.[Jian], Su, M.Z.[Ming-Zhan], Zhang, L.T.[Lan-Tian], Yang, Z.K.[Zhen-Kai], Kuang, Y.C.[Ying-Cai],
LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Kim, D.[Dahun], Woo, S.[Sanghyun], Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Dense Pixel-Level Interpretation of Dynamic Scenes With Video Panoptic Segmentation,
IP(31), 2022, pp. 5383-5395.
IEEE DOI 2208
Task analysis, Image segmentation, Measurement, Electron tubes, Semantics, Head, Benchmark testing, Video panoptic segmentation, scene parsing BibRef

Lv, K.F.[Ke-Feng], Zhang, Y.S.[Yong-Sheng], Yu, Y.[Ying], Zhang, Z.C.[Zhen-Chao], Li, L.[Lei],
Visual Localization and Target Perception Based on Panoptic Segmentation,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Tian, Z.[Zhi], Zhang, B.[Bowen], Chen, H.[Hao], Shen, C.H.[Chun-Hua],
Instance and Panoptic Segmentation Using Conditional Convolutions,
PAMI(45), No. 1, January 2023, pp. 669-680.
IEEE DOI 2212
Head, Magnetic heads, Image segmentation, Task analysis, Semantics, Convolutional codes, Detectors, Fully convolutional networks, panoptic segmentation BibRef

Wang, L.[Le], Liu, H.Z.[Hong-Zhen], Zhou, S.P.[San-Ping], Tang, W.[Wei], Hua, G.[Gang],
Instance Motion Tendency Learning for Video Panoptic Segmentation,
IP(32), 2023, pp. 764-778.
IEEE DOI 2301
Image segmentation, Motion segmentation, Task analysis, Tracking, Optical flow, Transformers, Target tracking, deep neural network BibRef

Chang, S.E.[Shuo-En], Chen, Y.[Yi], Yang, Y.C.[Yi-Cheng], Lin, E.T.[En-Ting], Hsiao, P.Y.[Pei-Yung], Fu, L.C.[Li-Chen],
SE-PSNet: Silhouette-based Enhancement Feature for Panoptic Segmentation Network,
JVCIR(90), 2023, pp. 103736.
Elsevier DOI 2301
Deep learning, Panoptic segmentation, Instance segmentation, Silhouette, confidence score BibRef

Li, Y.W.[Yan-Wei], Zhao, H.S.[Heng-Shuang], Qi, X.J.[Xiao-Juan], Chen, Y.[Yukang], Qi, L.[Lu], Wang, L.W.[Li-Wei], Li, Z.M.[Ze-Ming], Sun, J.[Jian], Jia, J.Y.[Jia-Ya],
Fully Convolutional Networks for Panoptic Segmentation With Point-Based Supervision,
PAMI(45), No. 4, April 2023, pp. 4552-4568.
IEEE DOI 2303
BibRef
Earlier: A1, A2, A3, A6, A7, A8, A9, Only:
Fully Convolutional Networks for Panoptic Segmentation,
CVPR21(214-223)
IEEE DOI 2111
Kernel, Annotations, Semantics, Image segmentation, Generators, Costs, Task analysis, Fully convolutional networks, point-based supervision. Convolutional codes, Location awareness, Semantics, Pipelines. BibRef

Lei, H.W.[Hai-Wei], He, F.Y.[Fang-Yuan], Jia, B.[Bohui], Wu, Q.[Qian],
MFNet: Panoptic segmentation network based on multiscale feature weighted fusion and frequency domain attention mechanism,
IET-CV(17), No. 1, 2023, pp. 88-97.
DOI Link 2303
BibRef

Jaus, A.[Alexander], Yang, K.L.[Kai-Lun], Stiefelhagen, R.[Rainer],
Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning,
ITS(24), No. 4, April 2023, pp. 4438-4453.
IEEE DOI 2304
Image segmentation, Task analysis, Training, Standards, Mobile agents, Semantics, Transformers, Panoptic segmentation, contrastive learning BibRef

Šaric, J.[Josip], Oršic, M.[Marin], Šegvic, S.[Siniša],
Panoptic SwiftNet: Pyramidal Fusion for Real-Time Panoptic Segmentation,
RS(15), No. 8, 2023, pp. 1968.
DOI Link 2305
BibRef

Chuang, Y.L.[Yue-Long], Zhang, S.Q.[Shi-Qing], Zhao, X.M.[Xiao-Ming],
Deep learning-based panoptic segmentation: Recent advances and perspectives,
IET-IPR(17), No. 10, 2023, pp. 2807-2828.
DOI Link 2308
computer vision, image segmentation BibRef

Xiang, B.B.[Bin-Bin], Yue, Y.[Yuanwen], Peters, T.[Torben], Schindler, K.[Konrad],
A Review of panoptic segmentation for mobile mapping point clouds,
PandRS(203), 2023, pp. 373-391.
Elsevier DOI 2310
Mobile mapping point clouds, 3D panoptic segmentation, 3D semantic segmentation, 3D instance segmentation, 3D deep learning backbones BibRef

Wang, H.[Hai], Qiu, M.[Meng], Cai, Y.F.[Ying-Feng], Chen, L.[Long], Li, Y.C.[Yi-Cheng],
Sparse U-PDP: A Unified Multi-Task Framework for Panoptic Driving Perception,
ITS(24), No. 10, October 2023, pp. 11308-11320.
IEEE DOI 2310
BibRef

Zhan, J.[Jiao], Luo, Y.[Yarong], Guo, C.[Chi], Wu, Y.[Yejun], Meng, J.W.[Jia-Wei], Liu, J.N.[Jing-Nan],
YOLOPX: Anchor-free multi-task learning network for panoptic driving perception,
PR(148), 2024, pp. 110152.
Elsevier DOI Code:
WWW Link. 2402
Multi-task learning, Panoptic driving perception, Autonomous driving, Anchor-free BibRef

Lin, G.C.[Guang-Chen], Li, S.Y.[Song-Yuan], Chen, Y.F.[Yi-Feng], Li, X.[Xi],
IDNet: Information Decomposition Network for Fast Panoptic Segmentation,
IP(33), 2024, pp. 1487-1496.
IEEE DOI Code:
WWW Link. 2402
Pipelines, Task analysis, Data mining, Feature extraction, Head, Semantic segmentation, Symbols, Scene parsing, panoptic segmentation BibRef

Hong, F.Z.[Fang-Zhou], Kong, L.D.[Ling-Dong], Zhou, H.[Hui], Zhu, X.G.[Xin-Ge], Li, H.S.[Hong-Sheng], Liu, Z.W.[Zi-Wei],
Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks,
PAMI(46), No. 5, May 2024, pp. 3480-3495.
IEEE DOI 2404
BibRef
Earlier: A1, A3, A4, A5, A6, Only:
LiDAR-based Panoptic Segmentation via Dynamic Shifting Network,
CVPR21(13085-13094)
IEEE DOI 2111
Point cloud compression, Task analysis, Laser radar, Semantics, Semantic segmentation, Feature extraction, point cloud semantic and instance segmentation. Measurement, Laser radar, Semantics, Robustness, Sensors BibRef


Shin, I.[Inkyu], Kim, D.[Dahun], Yu, Q.H.[Qi-Hang], Xie, J.[Jun], Kim, H.S.[Hong-Seok], Green, B.[Bradley], Kweon, I.S.[In So], Yoon, K.J.[Kuk-Jin], Chen, L.C.[Liang-Chieh],
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation,
WACV24(228-238)
IEEE DOI 2404
Adaptation models, Codes, Memory modules, Computer architecture, Streaming media, Transformers, Algorithms, Image recognition and understanding BibRef

Rashwan, A.[Abdullah], Zhang, J.[Jiageng], Taalimi, A.[Ali], Yang, F.[Fan], Zhou, X.Y.[Xing-Yi], Yan, C.C.[Chao-Chao], Chen, L.C.[Liang-Chieh], Li, Y.Q.[Ye-Qing],
MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation,
WACV24(840-850)
IEEE DOI 2404
Convolutional codes, Convolution, Semantics, Transformers, Vectors, Real-time systems, Mobile handsets, Algorithms BibRef

Richards, F.[Felix], Paiement, A.[Adeline], Xie, X.H.[Xiang-Hua], Sola, E.[Elisabeth], Duc, P.A.[Pierre-Alain],
Panoptic Segmentation of Galactic Structures in LSB Images,
MVA23(1-6)
DOI Link 2403
Training, Deep learning, Image segmentation, Visualization, Surface contamination, Training data, Object segmentation BibRef

Zhang, X.[Xiang], Chen, Z.[Zeyuan], Wei, F.[Fangyin], Tu, Z.W.[Zhuo-Wen],
Uni-3D: A Universal Model for Panoptic 3D Scene Reconstruction,
ICCV23(9222-9232)
IEEE DOI 2401
BibRef

Chen, X.[Xi], Li, S.[Shuang], Lim, S.N.[Ser-Nam], Torralba, A.[Antonio], Zhao, H.S.[Heng-Shuang],
Open-vocabulary Panoptic Segmentation with Embedding Modulation,
ICCV23(1141-1150)
IEEE DOI Code:
WWW Link. 2401
BibRef

Chen, T.[Ting], Li, L.[Lala], Saxena, S.[Saurabh], Hinton, G.[Geoffrey], Fleed, D.J.[David J.],
A Generalist Framework for Panoptic Segmentation of Images and Videos,
ICCV23(909-919)
IEEE DOI 2401
BibRef

Li, W.[Wentong], Yuan, Y.Q.[Yu-Qian], Wang, S.[Song], Zhu, J.[Jianke], Li, J.S.[Jian-Shu], Liu, J.[Jian], Zhang, L.[Lei],
Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport,
ICCV23(572-581)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhang, Z.W.[Zhi-Wei], Zhang, Z.Z.[Zhi-Zhong], Yu, Q.[Qian], Yi, R.[Ran], Xie, Y.[Yuan], Ma, L.Z.[Li-Zhuang],
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment,
ICCV23(3639-3648)
IEEE DOI Code:
WWW Link. 2401
BibRef

He, J.W.[Jun-Wen], Wang, Y.F.[Yi-Fan], Wang, L.J.[Li-Jun], Lu, H.C.[Hu-Chuan], Luo, B.[Bin], He, J.Y.[Jun-Yan], Lan, J.P.[Jin-Peng], Geng, Y.F.[Yi-Feng], Xie, X.[Xuansong],
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning,
ICCV23(4088-4098)
IEEE DOI Code:
WWW Link. 2401
BibRef

Saha, S.[Suman], Hoyer, L.[Lukas], Obukhov, A.[Anton], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation,
ICCV23(19177-19188)
IEEE DOI Code:
WWW Link. 2401
BibRef

Žust, L.[Lojze], Perš, J.[Janez], Kristan, M.[Matej],
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark,
ICCV23(20247-20257)
IEEE DOI Code:
WWW Link. 2401
BibRef

Zhu, M.H.[Ming-Han], Han, S.Z.[Shi-Zhong], Ghaffari, M.[Maani], Cai, H.[Hong], Porikli, F.M.[Fatih M.], Borse, S.[Shubhankar],
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction,
ICCV23(22431-22441)
IEEE DOI 2401
BibRef

Song, S.[Sumin], Sagong, M.C.[Min-Cheol], Jung, S.W.[Seung-Won], Ko, S.J.[Sung-Jea],
Semantic and Instance-Aware Pixel-Adaptive Convolution for Panoptic Segmentation,
ICIP23(16-20)
IEEE DOI 2312
BibRef

Sakaino, H.[Hidetomo],
PanopticRoad: Integrated Panoptic Road Segmentation Under Adversarial Conditions,
PVUW23(3591-3603)
IEEE DOI 2309
BibRef

Xu, J.R.[Jia-Rui], Liu, S.[Sifei], Vahdat, A.[Arash], Byeon, W.[Wonmin], Wang, X.L.[Xiao-Long], de Mello, S.[Shalini],
Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models,
CVPR23(2955-2966)
IEEE DOI 2309
BibRef

Choudhuri, A.[Anwesa], Chowdhary, G.[Girish], Schwing, A.G.[Alexander G.],
Context-Aware Relative Object Queries to Unify Video Instance and Panoptic Segmentation,
CVPR23(6377-6386)
IEEE DOI 2309
BibRef

Siddiqui, Y.[Yawar], Porzi, L.[Lorenzo], Bulň, S.R.[Samuel Rota], Müller, N.[Norman], Nießner, M.[Matthias], Dai, A.[Angela], Kontschieder, P.[Peter],
Panoptic Lifting for 3D Scene Understanding with Neural Fields,
CVPR23(9043-9052)
IEEE DOI 2309
BibRef

Zhang, J.Y.[Jing-Yi], Huang, J.X.[Jia-Xing], Zhang, X.Q.[Xiao-Qin], Lu, S.J.[Shi-Jian],
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration,
CVPR23(11227-11237)
IEEE DOI 2309
BibRef

Li, X.Y.[Xiao-Yan], Zhang, G.[Gang], Wang, B.Y.[Bo-Yue], Hu, Y.L.[Yong-Li], Yin, B.C.[Bao-Cai],
Center Focusing Network for Real-Time LiDAR Panoptic Segmentation,
CVPR23(13425-13434)
IEEE DOI 2309
BibRef

Hu, J.[Jie], Huang, L.Y.[Lin-Yan], Ren, T.[Tianhe], Zhang, S.C.[Sheng-Chuan], Ji, R.R.[Rong-Rong], Cao, L.J.[Liu-Juan],
You Only Segment Once: Towards Real-Time Panoptic Segmentation,
CVPR23(17819-17829)
IEEE DOI 2309
BibRef

Kachole, S.[Sanket], Alkendi, Y.[Yusra], Naeini, F.B.[Fariborz Baghaei], Makris, D.[Dimitrios], Zweiri, Y.[Yahya],
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network,
EventVision23(4083-4092)
IEEE DOI 2309
BibRef

Daza, L.[Laura], Pont-Tuset, J.[Jordi], Arbeláez, P.[Pablo],
Adversarially Robust Panoptic Segmentation (arpas) Benchmark,
AdvRob22(378-395).
Springer DOI 2304
BibRef

Kreuzberg, L.[Lars], Zulfikar, I.E.[Idil Esen], Mahadevan, S.[Sabarinath], Engelmann, F.[Francis], Leibe, B.[Bastian],
4d-stop: Panoptic Segmentation of 4d Lidar Using Spatio-temporal Object Proposal Generation and Aggregation,
AVVision22(537-553).
Springer DOI 2304
BibRef

Sun, B.[Bo], Kuen, J.[Jason], Lin, Z.[Zhe], Mordohai, P.[Philippos], Chen, S.[Simon],
PRN: Panoptic Refinement Network,
WACV23(3952-3962)
IEEE DOI 2302
Training, Image segmentation, Semantics, Refining, Predictive models, Algorithms: Image recognition and understanding (object detection, image and video synthesis BibRef

de Geus, D.[Daan], Dubbelman, G.[Gijs],
Intra-Batch Supervision for Panoptic Segmentation on High-Resolution Images,
WACV23(3164-3172)
IEEE DOI 2302
Training, Measurement, Image segmentation, Crops, Task analysis, Algorithms: Image recognition and understanding (object detection, segmentation) BibRef

Petrovai, A.[Andra], Nedevschi, S.[Sergiu],
MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation,
WACV23(3076-3085)
IEEE DOI 2302
Training, Image segmentation, Motion segmentation, Video sequences, Semantics, Estimation, Algorithms: 3D computer vision BibRef

Fan, J.S.[Jun-Song], Zhang, Z.X.[Zhao-Xiang], Tan, T.N.[Tie-Niu],
Pointly-Supervised Panoptic Segmentation,
ECCV22(XXX:319-336).
Springer DOI 2211
BibRef

Xu, S.L.[Shi-Lin], Li, X.T.[Xiang-Tai], Yang, Y.[Yibo], Li, H.Y.[Hong-Yang], Cheng, G.L.[Guang-Liang], Tong, Y.H.[Yun-Hai],
Query Learning of Both Thing and Stuff for Panoptic Segmentation,
ICIP22(716-720)
IEEE DOI 2211
Training, Image segmentation, Schedules, Image coding, Design methodology, Pipelines, Semantics, Panoptic segmentation, Computer vision BibRef

Liu, Q.F.[Qing-Feng], El-Khamy, M.[Mostafa],
Panoptic-Deeplab-DVA: Improving Panoptic Deeplab with Dual Value Attention and Instance Boundary Aware Regression,
ICIP22(3888-3892)
IEEE DOI 2211
Training, Performance evaluation, Mobile handsets, Complexity theory, Task analysis, Information exchange, Panoptic DeepLab BibRef

Mei, J.[Jieru], Zhu, A.Z.[Alex Zihao], Yan, X.C.[Xin-Chen], Yan, H.[Hang], Qiao, S.Y.[Si-Yuan], Chen, L.C.[Liang-Chieh], Kretzschmar, H.[Henrik],
Waymo Open Dataset: Panoramic Video Panoptic Segmentation,
ECCV22(XXIX:53-72).
Springer DOI 2211
BibRef

Li, X.T.[Xiang-Tai], Xu, S.L.[Shi-Lin], Yang, Y.[Yibo], Cheng, G.L.[Guang-Liang], Tong, Y.H.[Yun-Hai], Tao, D.C.[Da-Cheng],
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation,
ECCV22(XXVII:729-747).
Springer DOI 2211
BibRef

Yuan, H.[Haobo], Li, X.T.[Xiang-Tai], Yang, Y.[Yibo], Cheng, G.L.[Guang-Liang], Zhang, J.[Jing], Tong, Y.H.[Yun-Hai], Zhang, L.[Lefei], Tao, D.C.[Da-Cheng],
PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation,
ECCV22(XXVII:582-599).
Springer DOI 2211
BibRef

Kundu, A.[Abhijit], Genova, K.[Kyle], Yin, X.Q.[Xiao-Qi], Fathi, A.[Alireza], Pantofaru, C.[Caroline], Guibas, L.J.[Leonidas J.], Tagliasacchi, A.[Andrea], Dellaert, F.[Frank], Funkhouser, T.[Thomas],
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation,
CVPR22(12861-12871)
IEEE DOI 2210
Image segmentation, Solid modeling, Semantics, Color, Predictive models, Rendering (computer graphics), Scene analysis and understanding BibRef

Zhou, Y.[Yi], Zhang, H.[Hui], Lee, H.[Hana], Sun, S.Y.[Shu-Yang], Li, P.J.[Ping-Jun], Zhu, Y.G.[Yang-Guang], Yoo, B.I.[Byung-In], Qi, X.J.[Xiao-Juan], Han, J.J.[Jae-Joon],
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation,
CVPR22(3083-3093)
IEEE DOI 2210
Representation learning, Tracking, Semantics, Pipelines, Benchmark testing, Pattern recognition, Motion and tracking BibRef

Graber, C.[Colin], Jazra, C.[Cyril], Luo, W.J.[Wen-Jie], Gui, L.Y.[Liang-Yan], Schwing, A.[Alexander],
Joint Forecasting of Panoptic Segmentations with Difference Attention,
CVPR22(2617-2626)
IEEE DOI 2210
BibRef
And: Precognition22(2558-2567)
IEEE DOI 2210
Measurement, Image analysis, Shape, Predictive models, Transformers, Pattern recognition, Scene analysis and understanding, grouping and shape analysis BibRef

Gao, N.[Naiyu], He, F.[Fei], Jia, J.[Jian], Shan, Y.[Yanhu], Zhang, H.Y.[Hao-Yang], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi],
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation,
CVPR22(1622-1632)
IEEE DOI 2210
Image segmentation, Head, Semantics, Estimation, Lead, Pattern recognition, 3D from single images, Video analysis and understanding BibRef

Borse, S.[Shubhankar], Park, H.[Hyojin], Cai, H.[Hong], Das, D.[Debasmit], Garrepalli, R.[Risheek], Porikli, F.M.[Fatih M.],
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation,
CVPR22(1259-1269)
IEEE DOI 2210
Visualization, Roads, Semantics, Computer architecture, Benchmark testing, Feature extraction, Segmentation, Representation learning BibRef

Fazlali, H.[Hamidreza], Xu, Y.X.[Yi-Xuan], Ren, Y.[Yuan], Liu, B.B.[Bing-Bing],
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation,
CVPR22(17171-17180)
IEEE DOI 2210
Heating systems, Laser radar, Semantics, Object detection, Performance gain, Feature extraction, Vision applications and systems BibRef

Mohan, R.[Rohit], Valada, A.[Abhinav],
Amodal Panoptic Segmentation,
CVPR22(20991-21000)
IEEE DOI 2210
Measurement, Computational modeling, Semantics, Computer architecture, Benchmark testing, Pattern recognition, Scene analysis and understanding BibRef

Miao, J.[Jiaxu], Wang, X.H.[Xiao-Han], Wu, Y.[Yu], Li, W.[Wei], Zhang, X.[Xu], Wei, Y.C.[Yun-Chao], Yang, Y.[Yi],
Large-scale Video Panoptic Segmentation in the Wild: A Benchmark,
CVPR22(21001-21011)
IEEE DOI 2210
Annotations, Shape, Semantics, Benchmark testing, Pattern recognition, Task analysis, Datasets and evaluation, grouping and shape analysis BibRef

Zendel, O.[Oliver], Schörghuber, M.[Matthias], Rainer, B.[Bernhard], Murschitz, M.[Markus], Beleznai, C.[Csaba],
Unifying Panoptic Segmentation for Autonomous Driving,
CVPR22(21319-21328)
IEEE DOI 2210
Training, Visualization, Semantics, Data visualization, Benchmark testing, Licenses, Robustness, Datasets and evaluation, grouping and shape analysis BibRef

Chen, Q.[Qi], Vora, S.[Sourabh],
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity,
WAD22(4528-4535)
IEEE DOI 2210
Laser radar, Semantics, Merging, Clustering algorithms, Object detection BibRef

Li, Z.Q.[Zhi-Qi], Wang, W.[Wenhai], Xie, E.[Enze], Yu, Z.D.[Zhi-Ding], Anandkumar, A.[Anima], Alvarez, J.M.[Jose M.], Luo, P.[Ping], Lu, T.[Tong],
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers,
CVPR22(1270-1279)
IEEE DOI 2210
Training, Image segmentation, Costs, Semantics, Interference, Transformers, Segmentation, grouping and shape analysis, Scene analysis and understanding BibRef

Li, J.[Jinke], He, X.[Xiao], Wen, Y.[Yang], Gao, Y.[Yuan], Cheng, X.Q.[Xiao-Qiang], Zhang, D.[Dan],
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap,
CVPR22(11799-11808)
IEEE DOI 2210
Heating systems, Laser radar, Fuses, Shape, Navigation, Semantics, grouping and shape analysis, Segmentation BibRef

Raivio, L.[Leevi], Rahtu, E.[Esa],
Online Panoptic 3D Reconstruction as a Linear Assignment Problem,
CIAP22(II:39-50).
Springer DOI 2205
BibRef

Quattrocchi, C.[Camillo], Mauro, D.D.[Daniele Di], Furnari, A.[Antonino], Farinella, G.M.[Giovanni Maria],
Panoptic Segmentation in Industrial Environments Using Synthetic and Real Data,
CIAP22(II:275-286).
Springer DOI 2205
BibRef

Hwang, S.[Sukjun], Oh, S.W.[Seoung Wug], Kim, S.J.[Seon Joo],
Single-shot Path Integrated Panoptic Segmentation,
WACV22(1939-1948)
IEEE DOI 2202
Computational modeling, Semantics, Benchmark testing, Information filters, Task analysis, Scene Understanding BibRef

Petrovai, A.[Andra], Nedevschi, S.[Sergiu],
Time-Space Transformers for Video Panoptic Segmentation,
WACV22(2643-2652)
IEEE DOI 2202
Image resolution, Correlation, Computational modeling, Aggregates, Semantics, Computer architecture, Transformers, Segmentation, Vision Systems and Applications BibRef

Zhao, Y.M.[Yi-Ming], Zhang, X.[Xiao], Huang, X.M.[Xin-Ming],
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation,
TradiCV21(2464-2473)
IEEE DOI 2112
Deep learning, Laser radar, Codes, Semantics, Pipelines, Clustering algorithms BibRef

Kerola, T.[Tommi], Li, J.[Jie], Kanehira, A.[Atsushi], Kudo, Y.[Yasunori], Vallet, A.[Alexis], Gaidon, A.[Adrien],
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation,
CVPR21(14408-14418)
IEEE DOI 2111
Semantics, Fasteners, Predictive models, Ontologies, Stability analysis, Pattern recognition, Proposals BibRef

Shen, Y.H.[Yun-Hang], Cao, L.J.[Liu-Juan], Chen, Z.W.[Zhi-Wei], Lian, F.H.[Fei-Hong], Zhang, B.C.[Bao-Chang], Su, C.[Chi], Wu, Y.J.[Yong-Jian], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation,
CVPR21(16689-16700)
IEEE DOI 2111
Location awareness, Image segmentation, Semantics, Spatial coherence, Object detection, Feature extraction BibRef

Zhou, Z.X.[Zi-Xiang], Zhang, Y.[Yang], Foroosh, H.[Hassan],
Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation,
CVPR21(13189-13198)
IEEE DOI 2111
Laser radar, Semantics, Real-time systems, Pattern recognition, Complexity theory BibRef

de Geus, D.[Daan], Meletis, P.[Panagiotis], Lu, C.Y.[Chen-Yang], Wen, X.X.[Xiao-Xiao], Dubbelman, G.[Gijs],
Part-aware Panoptic Segmentation,
CVPR21(5481-5490)
IEEE DOI 2111
Measurement, Training, Technological innovation, Codes, Annotations, Merging BibRef

Yu, Q.H.[Qi-Hang], Wang, H.Y.[Hui-Yu], Kim, D.[Dahun], Qiao, S.Y.[Si-Yuan], Collins, M.[Maxwell], Zhu, Y.K.[Yu-Kun], Adam, H.[Hartwig], Yuille, A.Y.[Alan Y.], Chen, L.C.[Liang-Chieh],
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation,
CVPR22(2550-2560)
IEEE DOI 2210
Art, Computer architecture, Transformers, Pattern recognition, Task analysis, Segmentation, grouping and shape analysis BibRef

Wang, H.Y.[Hui-Yu], Zhu, Y.K.[Yu-Kun], Adam, H.[Hartwig], Yuille, A.L.[Alan L.], Chen, L.C.[Liang-Chieh],
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers,
CVPR21(5459-5470)
IEEE DOI 2111
Merging, Pipelines, Computer architecture, Transformers, Pattern recognition, Task analysis BibRef

Qiao, S.Y.[Si-Yuan], Zhu, Y.K.[Yu-Kun], Adam, H.[Hartwig], Yuille, A.L.[Alan L.], Chen, L.C.[Liang-Chieh],
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation,
CVPR21(3996-4007)
IEEE DOI 2111
Measurement, Solid modeling, Semantics, Estimation, Predictive models, Pattern recognition BibRef

Woo, S.[Sanghyun], Kim, D.[Dahun], Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Learning to Associate Every Segment for Video Panoptic Segmentation,
CVPR21(2704-2713)
IEEE DOI 2111
Learning systems, Computational modeling, Linear programming, Pattern recognition, Proposals, Task analysis BibRef

Hwang, J.[Jaedong], Oh, S.W.[Seoung Wug], Lee, J.Y.[Joon-Young], Han, B.H.[Bo-Hyung],
Exemplar-Based Open-Set Panoptic Segmentation Network,
CVPR21(1175-1184)
IEEE DOI 2111
Training, Image segmentation, Solid modeling, Benchmark testing, Solids, Pattern recognition BibRef

Aygün, M.[Mehmet], Ošep, A.[Aljoša], Weber, M.[Mark], Maximov, M.[Maxim], Stachniss, C.[Cyrill], Behley, J.[Jens], Leal-Taixé, L.[Laura],
4D Panoptic LiDAR Segmentation,
CVPR21(5523-5533)
IEEE DOI 2111
Measurement, Laser radar, Roads, Computational modeling, Semantics, Benchmark testing BibRef

Porzi, L.[Lorenzo], Bulň, S.R.[Samuel Rota], Kontschieder, P.[Peter],
Improving Panoptic Segmentation at All Scales,
CVPR21(7298-7307)
IEEE DOI 2111
Training, Measurement, Image segmentation, Image resolution, Memory management, Crops BibRef

Huang, J.X.[Jia-Xing], Guan, D.[Dayan], Xiao, A.[Aoran], Lu, S.J.[Shi-Jian],
Cross-View Regularization for Domain Adaptive Panoptic Segmentation,
CVPR21(10128-10139)
IEEE DOI 2111
Image segmentation, Adaptive systems, Semantics, Supervised learning, Pattern recognition, Task analysis BibRef

Graber, C.[Colin], Tsai, G.[Grace], Firman, M.[Michael], Brostow, G.[Gabriel], Schwing, A.[Alexander],
Panoptic Segmentation Forecasting,
CVPR21(12512-12521)
IEEE DOI 2111
Image segmentation, Motion segmentation, Semantics, Dynamics, Predictive models, Cameras, Real-time systems BibRef

Hong, W.X.[Wei-Xiang], Guo, Q.P.[Qing-Pei], Zhang, W.[Wei], Chen, J.D.[Jing-Dong], Chu, W.[Wei],
LPSNet: A lightweight solution for fast panoptic segmentation,
CVPR21(16741-16749)
IEEE DOI 2111
Costs, Semantics, Memory management, Pipelines, Object detection, Real-time systems BibRef

Bonde, U.[Ujwal], Alcantarilla, P.F.[Pablo F.], Leutenegger, S.[Stefan],
Towards Bounding-Box Free Panoptic Segmentation,
GCPR20(316-330).
Springer DOI 2110
BibRef

Graber, C.[Colin], Tsai, G.[Grace], Firman, M.[Michael], Brostow, G.[Gabriel], Schwing, A.[Alexander],
Panoptic Segmentation Forecasting,
Precognition21(2279-2288)
IEEE DOI 2109
Image segmentation, Motion segmentation, Semantics, Dynamics, Predictive models, Cameras, Real-time systems BibRef

Chang, C.Y.[Chia-Yuan], Chang, S.E.[Shuo-En], Hsiao, P.Y.[Pei-Yung], Fu, L.C.[Li-Chen],
Epsnet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion,
ACCV20(I:689-705).
Springer DOI 2103
BibRef

Qin, Z.Q.[Ze-Qun], Zhang, P.Y.[Peng-Yi], Wu, F.[Fei], Li, X.[Xi],
FcaNet: Frequency Channel Attention Networks,
ICCV21(763-772)
IEEE DOI 2203
Image segmentation, Codes, Frequency-domain analysis, Computational modeling, Object detection, BibRef

Chen, Y.F.[Yi-Feng], Lin, G.C.[Guang-Chen], Li, S.Y.[Song-Yuan], Bourahla, O.[Omar], Wu, Y.M.[Yi-Ming], Wang, F.F.[Fang-Fang], Feng, J.Y.[Jun-Yi], Xu, M.L.[Ming-Liang], Li, X.[Xi],
BANet: Bidirectional Aggregation Network With Occlusion Handling for Panoptic Segmentation,
CVPR20(3792-3801)
IEEE DOI 2008
Semantics, Image segmentation, Agriculture, Task analysis, Feature extraction, Pipelines, Convolution BibRef

Dundar, A., Sapra, K., Liu, G., Tao, A., Catanzaro, B.,
Panoptic-Based Image Synthesis,
CVPR20(8067-8076)
IEEE DOI 2008
Convolution, Semantics, Image generation, Task analysis, Generators, Image resolution, Windows BibRef

Hou, R., Li, J., Bhargava, A., Raventos, A., Guizilini, V., Fang, C., Lynch, J., Gaidon, A.,
Real-Time Panoptic Segmentation From Dense Detections,
CVPR20(8520-8529)
IEEE DOI 2008
Semantics, Real-time systems, Image segmentation, Task analysis, Object detection, Proposals, Prediction algorithms BibRef

Wu, Y., Zhang, G., Gao, Y., Deng, X., Gong, K., Liang, X., Lin, L.,
Bidirectional Graph Reasoning Network for Panoptic Segmentation,
CVPR20(9077-9086)
IEEE DOI 2008
Image segmentation, Semantics, Cognition, Task analysis, Feature extraction, Visualization, Proposals BibRef

Wang, H., Luo, R., Maire, M., Shakhnarovich, G.,
Pixel Consensus Voting for Panoptic Segmentation,
CVPR20(9461-9470)
IEEE DOI 2008
Semantics, Transforms, Heating systems, Feature extraction, Image segmentation, Task analysis, Training BibRef

Kim, D., Woo, S., Lee, J., Kweon, I.S.,
Video Panoptic Segmentation,
CVPR20(9856-9865)
IEEE DOI 2008
Task analysis, Image segmentation, Electron tubes, Measurement, Semantics, Head BibRef

Lazarow, J., Lee, K., Shi, K., Tu, Z.,
Learning Instance Occlusion for Panoptic Segmentation,
CVPR20(10717-10726)
IEEE DOI 2008
Head, Semantics, Image segmentation, Proposals, Task analysis, Nickel BibRef

Cheng, B., Collins, M.D., Zhu, Y., Liu, T., Huang, T.S., Adam, H., Chen, L.,
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation,
CVPR20(12472-12482)
IEEE DOI 2008
Semantics, Image segmentation, Decoding, Task analysis, Spatial resolution, Convolution, Feature extraction BibRef

Li, Q., Qi, X., Torr, P.H.S.,
Unifying Training and Inference for Panoptic Segmentation,
CVPR20(13317-13325)
IEEE DOI 2008
Semantics, Training, Head, Pipelines, Feature extraction, Object detection, Image segmentation BibRef

Liu, D., Zhang, D., Song, Y., Zhang, F., O'Donnell, L., Huang, H., Chen, M., Cai, W.,
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting,
CVPR20(4242-4251)
IEEE DOI 2008
Image segmentation, Task analysis, Semantics, Microscopy, Adaptation models, Object detection, Training BibRef

Kirillov, A.[Alexander], He, K.[Kaiming], Girshick, R.[Ross], Rother, C.[Carsten], Dollar, P.[Piotr],
Panoptic Segmentation,
CVPR19(9396-9405).
IEEE DOI 2002
BibRef

Liu, H.Y.[Huan-Yu], Peng, C.[Chao], Yu, C.Q.[Chang-Qian], Wang, J.B.[Jing-Bo], Liu, X.[Xu], Yu, G.[Gang], Jiang, W.[Wei],
An End-To-End Network for Panoptic Segmentation,
CVPR19(6165-6174).
IEEE DOI 2002
BibRef

Li, Y.W.[Yan-Wei], Chen, X.[Xinze], Zhu, Z.[Zheng], Xie, L.X.[Ling-Xi], Huang, G.[Guan], Du, D.L.[Da-Long], Wang, X.G.[Xin-Gang],
Attention-Guided Unified Network for Panoptic Segmentation,
CVPR19(7019-7028).
IEEE DOI 2002
BibRef

Xiong, Y.[Yuwen], Liao, R.J.[Ren-Jie], Zhao, H.S.[Heng-Shuang], Hu, R.[Rui], Bai, M.[Min], Yumer, E.[Ersin], Urtasun, R.[Raquel],
UPSNet: A Unified Panoptic Segmentation Network,
CVPR19(8810-8818).
IEEE DOI 2002
BibRef

Li, Q.Z.[Qi-Zhu], Arnab, A.[Anurag], Torr, P.H.S.[Philip H. S.],
Weakly- and Semi-supervised Panoptic Segmentation,
ECCV18(XV: 106-124).
Springer DOI 1810
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Co-Segmentation, Cosegmentation .


Last update:May 23, 2024 at 14:31:23