Liu, D.,
Zhang, D.,
Song, Y.,
Huang, H.,
Cai, W.,
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm
for Biomedical and Biological Images,
IP(30), 2021, pp. 2045-2059.
IEEE DOI
2101
Semantics, Image segmentation, Task analysis, Biology,
Biomedical imaging, Histopathology,
plant phenotype images
BibRef
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.K.[Yu-Kang],
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
image segmentation
BibRef
Xiang, B.B.[Bin-Bin],
Yue, Y.W.[Yuan-Wen],
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
Ying, Z.M.[Zhong-Mou],
Yuan, X.F.[Xian-Feng],
Song, B.[Boyi],
Song, Y.[Yong],
Zhou, F.Y.[Feng-Yu],
Sheng, W.H.[Wei-Hua],
Accurate and Efficient 3D Panoptic Mapping Using Diverse Information
Modalities and Multidimensional Data Association,
CirSysVideo(34), No. 6, June 2024, pp. 4489-4502.
IEEE DOI
2406
Semantics, Image segmentation, Image reconstruction, Real-time systems,
Object detection, Semantic segmentation, panoptic inference
BibRef
van Heusden, R.[Ruben],
Marx, M.[Maarten],
A sharper definition of alignment for Panoptic Quality,
PRL(185), 2024, pp. 87-93.
Elsevier DOI
2410
Panoptic quality, Image segmentation, Partitioning
BibRef
Zhao, L.[Lin],
Chen, S.[Sijia],
Tang, X.[Xu],
Tao, W.B.[Wen-Bing],
DualGroup for 3D instance and panoptic segmentation,
PRL(185), 2024, pp. 124-129.
Elsevier DOI
2410
3D instance segmentation, Point cloud, ECSVL, DHG, DualGroup
BibRef
Li, X.T.[Xiang-Tai],
Xu, S.L.[Shi-Lin],
Yang, Y.[Yibo],
Yuan, H.[Haobo],
Cheng, G.L.[Guang-Liang],
Tong, Y.H.[Yun-Hai],
Lin, Z.C.[Zhou-Chen],
Yang, M.H.[Ming-Hsuan],
Tao, D.C.[Da-Cheng],
Panoptic-PartFormer++: A Unified and Decoupled View for Panoptic Part
Segmentation,
PAMI(46), No. 12, December 2024, pp. 11087-11103.
IEEE DOI
2411
BibRef
Earlier: A1, A2, A3, A5, A6, A9, Only:
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part
Segmentation,
ECCV22(XXVII:729-747).
Springer DOI
2211
Task analysis, Image segmentation, Measurement, Transformers,
Computational modeling, Decoding, Feature extraction, vision transformer
BibRef
Zhou, Y.[Yi],
Zhang, H.[Hui],
Park, S.I.[Seung-In],
Yoo, B.[ByungIn],
Qi, X.J.[Xiao-Juan],
Object-Centric Representation Learning for Video Scene Understanding,
PAMI(46), No. 12, December 2024, pp. 8410-8423.
IEEE DOI
2411
Semantics, Task analysis, IP networks, Feature extraction, Pipelines,
Estimation, Generators, Scene understanding, object-centric representation
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,
Motion and tracking
BibRef
Liu, Z.R.[Zhuo-Ran],
Li, Z.Z.[Zi-Zhen],
Liang, Y.[Ying],
Persello, C.[Claudio],
Sun, B.[Bo],
He, G.[Guangjun],
Ma, L.[Lei],
RSPS-SAM: A Remote Sensing Image Panoptic Segmentation Method Based
on SAM,
RS(16), No. 21, 2024, pp. 4002.
DOI Link
2411
BibRef
Chen, L.W.[Lin-Wei],
Fu, Y.[Ying],
Gu, L.[Lin],
Yan, C.G.[Cheng-Gang],
Harada, T.[Tatsuya],
Huang, G.[Gao],
Frequency-Aware Feature Fusion for Dense Image Prediction,
PAMI(46), No. 12, December 2024, pp. 10763-10780.
IEEE DOI
2411
Generators, Task analysis, Feature extraction, Standards,
Instance segmentation, Semantic segmentation, Object detection,
panoptic segmentation
BibRef
Xiao, Z.Q.[Ze-Qi],
Zhang, W.W.[Wen-Wei],
Wang, T.[Tai],
Loy, C.C.[Chen Change],
Lin, D.H.[Da-Hua],
Pang, J.M.[Jiang-Miao],
Position-Guided Point Cloud Panoptic Segmentation Transformer,
IJCV(133), No. 1, January 2025, pp. 275-290.
Springer DOI
2501
BibRef
Lin, Z.Q.[Zhong-Qi],
Jiang, X.D.[Xu-Dong],
Zheng, Z.[Zengwei],
Line-of-Sight Depth Attention for Panoptic Parsing of Distant
Small-Faint Instances,
IP(34), 2025, pp. 1354-1366.
IEEE DOI
2503
Transformers, Line-of-sight propagation, Image segmentation,
Semantics, Feature extraction, Correlation, Training, Pipelines,
small-faint instance
BibRef
Zhao, M.[Ming],
Zhang, J.[Jing],
Panoptic segmentation-based semantic embedding matching model for
scene graph generation,
PRL(193), 2025, pp. 56-63.
Elsevier DOI
2505
Panoptic segmentation, Scene graph generation,
Semantic embedding, Graph neural network
BibRef
Stolle, K.H.W.[Kurt H.W.],
Balancing Shared and Task-Specific Representations: A Hybrid Approach
to Depth-Aware Video Panoptic Segmentation,
WACV25(3301-3309)
IEEE DOI
2505
Visualization, Technological innovation, Depth measurement, Scalability,
Multitasking, Transformers, Decoding, Object tracking, object tracking
BibRef
Zhu, R.S.[Run-Song],
Qiu, S.[Shi],
Wu, Q.Y.[Qian-Yi],
Hui, K.H.[Ka-Hei],
Heng, P.A.[Pheng-Ann],
Fu, C.W.[Chi-Wing],
PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion,
ECCV24(II: 92-108).
Springer DOI
2412
BibRef
Chen, J.P.[Jin-Peng],
Cong, R.M.[Run-Min],
Luo, Y.X.[Yu-Xuan],
Ip, H.H.S.[Horace Ho Shing],
Kwong, S.[Sam],
Strike a Balance in Continual Panoptic Segmentation,
ECCV24(XLI: 126-142).
Springer DOI
2412
BibRef
Lorenz, J.[Julian],
Pest, A.[Alexander],
Kienzle, D.[Daniel],
Ludwig, K.[Katja],
Lienhart, R.[Rainer],
A Fair Ranking and New Model for Panoptic Scene Graph Generation,
ECCV24(LXI: 148-164).
Springer DOI
2412
BibRef
Martinovic, I.[Ivan],
Šaric, J.[Josip],
Šegvic, S.[Siniša],
MC-PANDA: Mask Confidence for Panoptic Domain Adaptation,
ECCV24(LXXII: 167-185).
Springer DOI
2412
BibRef
Xiao, Z.[Zihao],
Jing, L.L.[Long-Long],
Wu, S.X.[Shang-Xuan],
Zhu, A.Z.[Alex Zihao],
Ji, J.W.[Jing-Wei],
Jiang, C.M.[Chiyu Max],
Hung, W.C.[Wei-Chih],
Funkhouser, T.[Thomas],
Kuo, W.C.[Wei-Cheng],
Angelova, A.[Anelia],
Zhou, Y.[Yin],
Sheng, S.W.[Shi-Wei],
3d Open-vocabulary Panoptic Segmentation with 2d-3d Vision-language
Distillation,
ECCV24(XL: 21-38).
Springer DOI
2412
BibRef
Li, J.[Jing],
Fan, J.S.[Jun-Song],
Zhang, Z.X.[Zhao-Xiang],
Point-supervised Panoptic Segmentation via Estimating Pseudo Labels
from Learnable Distance,
ECCV24(XVI: 95-112).
Springer DOI
2412
BibRef
van Gansbeke, W.[Wouter],
de Brabandere, B.[Bert],
A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask
Inpainting,
ECCV24(XV: 78-97).
Springer DOI
2412
BibRef
Zhou, Z.J.[Zi-Jian],
Zhu, Z.[Zheng],
Caesar, H.[Holger],
Shi, M.J.[Miao-Jing],
Openpsg: Open-set Panoptic Scene Graph Generation via Large Multimodal
Models,
ECCV24(X: 199-215).
Springer DOI
2412
BibRef
Wu, D.[Dong],
Yan, Z.[Zike],
Zha, H.B.[Hong-Bin],
PanoRecon: Real-Time Panoptic 3D Reconstruction from Monocular Video,
CVPR24(21507-21518)
IEEE DOI Code:
WWW Link.
2410
Geometry, Instance segmentation, Semantic segmentation, Semantics,
Streaming media, Reconstruction algorithms, 3D Reconstruction,
3D Panoptic Segmentation
BibRef
Le, D.T.[Duy Tho],
Gou, C.[Chenhui],
Datta, S.[Stavya],
Shi, H.[Hengcan],
Reid, I.D.[Ian D.],
Cai, J.F.[Jian-Fei],
Rezatofighi, H.[Hamid],
JRDB-PanoTrack: An Open-World Panoptic Segmentation and Tracking
Robotic Dataset in Crowded Human Environments,
CVPR24(22325-22334)
IEEE DOI
2410
Visualization, Navigation, Tracking, Benchmark testing,
Robot sensing systems, Sensor systems, Open World,
Robotic
BibRef
Chen, H.[Hao],
Hou, Y.Q.[Yu-Qi],
Qu, C.Y.[Chen-Yuan],
Testini, I.[Irene],
Hong, X.H.[Xiao-Han],
Jiao, J.B.[Jian-Bo],
360+x: A Panoptic Multi-modal Scene Understanding Dataset,
CVPR24(19373-19382)
IEEE DOI
2410
Annotations, Databases, Computational modeling,
Self-supervised learning, Manuals, Benchmark testing, Dataset,
360
BibRef
Wang, Y.Q.[Yu-Qi],
Chen, Y.T.[Yun-Tao],
Liao, X.Y.[Xing-Yu],
Fan, L.[Lue],
Zhang, Z.X.[Zhao-Xiang],
PanoOcc: Unified Occupancy Representation for Camera-based 3D
Panoptic Segmentation,
CVPR24(17158-17168)
IEEE DOI Code:
WWW Link.
2410
Representation learning, Location awareness, Solid modeling,
Semantic segmentation, Roads, Estimation, Occupancy prediction,
Camera-based 3D panoptic segmentation
BibRef
Cao, A.Q.[Anh-Quan],
Dai, A.[Angela],
de Charette, R.[Raoul],
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness,
CVPR24(14554-14564)
IEEE DOI Code:
WWW Link.
2410
Geometry, Uncertainty, Codes, Semantics, Estimation,
Panoptic Scene Completion, Uncertainty Estimation,
Efficient ensembling
BibRef
Kim, B.[Beomyoung],
Yu, J.[Joonsang],
Hwang, S.J.[Sung Ju],
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with
Visual Prompt Tuning,
CVPR24(3346-3356)
IEEE DOI Code:
WWW Link.
2410
Training, Visualization, Adaptation models,
Computational modeling, Semantics, panoptic segmentation,
visual prompt tuning
BibRef
de Geus, D.[Daan],
Dubbelman, G.[Gijs],
Task-Aligned Part-Aware Panoptic Segmentation Through Joint
Object-Part Representations,
CVPR24(3174-3183)
IEEE DOI
2410
Image segmentation, Accuracy,
panoptic segmentation, semantic segmentation, scene understanding
BibRef
Robert, D.[Damien],
Raguet, H.[Hugo],
Landrieu, L.[Loic],
Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering,
3DV24(179-189)
IEEE DOI Code:
WWW Link.
2408
Point cloud compression, Training, Solid modeling,
Adaptation models, Codes, 3D panoptic segmentation, 3D point cloud, superpoint
BibRef
Fu, X.[Xiao],
Zhang, S.Z.[Shang-Zhan],
Chen, T.R.[Tian-Run],
Lu, Y.C.[Yi-Chong],
Zhu, L.Y.[Lan-Yun],
Zhou, X.W.[Xiao-Wei],
Geiger, A.[Andreas],
Liao, Y.[Yiyi],
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene
Segmentation,
3DV22(1-11)
IEEE DOI
2408
Geometry, Training, Annotations, Semantics, Training data,
Rendering (computer graphics)
BibRef
Deery, J.[Jacob],
Lee, C.W.[Chang Won],
Waslander, S.L.[Steven L.],
ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic
Segmentation,
CRV23(137-144)
IEEE DOI
2406
Measurement, Deep learning, Image segmentation, Uncertainty,
Semantics, Estimation, Probabilistic logic, panoptic segmentation,
evidential deep learning
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,
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.Y.[Ze-Yuan],
Wei, F.Y.[Fang-Yin],
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
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LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and
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IEEE DOI Code:
WWW Link.
2401
BibRef
Zhu, M.H.[Ming-Han],
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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
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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,
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
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
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,
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,
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,
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,
Benchmark testing,
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,
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, 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, 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,
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, Transformers,
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,
Transformers, 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, 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, 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
Liu, X.L.[Xiao-Long],
Hou, Y.Q.[Yu-Qing],
Yao, A.[Anbang],
Chen, Y.R.[Yu-Rong],
Li, K.Q.[Ke-Qiang],
CASNet: Common Attribute Support Network for image instance and
panoptic segmentation,
ICPR21(8469-8475)
IEEE DOI
2105
Training, Bridges, Image segmentation, Semantics,
Clustering algorithms, Object detection, Prediction algorithms
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, M.E.[M. 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 .