The DOI matches its Crossref record; 56 of the 62 references it cites were found in a registry or an index.
Photographic Image Synthesis with Cascaded Refinement Networks
Open accessAbstract
We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This opens interesting research opportunities for the adaptation of lab-generated simulation to real-world conditions, similar to what has been observed in the field of image synthesis for self driving143 and robotics144 in the last decade.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [143]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
Flagged first, then by citations
6 of the 62 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- ba, 2016, Layer normalization
- maas, 2013, Rectifier nonlinearities improve neural network acoustic models, ICML
Cited by
Sorted by citations
Items 501–600 of 886
Sketch to portrait generation with generative adversarial networks and edge constraint
Liu, Q., Zhao, H., Wang, Y., Zhang, F., Ramasamy, M., & Qiao, Z. G. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107338 · Cited by 10
Chen, W., Li, Y., & Zhao, Z. (2022) · Sensors · doi:10.3390/s22051886 · Cited by 10
Lightweight multi-scale convolutional neural network for real time stereo matching
Xue, Y., Zhang, D., Li, L., Li, S., & Wang, Y. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104510 · Cited by 10
Trace Controlled Text to Image Generation
Yan, K., Ji, L., Wu, C., Bao, J., Zhou, M., Duan, N., & Ma, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_4 · Cited by 10
Generative View Synthesis: From Single-view Semantics to Novel-view ImagesPreprint
Habtegebrial, T., Jampani, V., Gallo, O., & Stricker, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09106 · Cited by 10
Zhang, H., Yang, Y.-J., & Zeng, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124132 · Cited by 9
Universal Filter-Based Lightweight Image Enhancement Model with Unpaired Learning Mode
Samarin, A., Nazarenko, A., Toropov, A., Kotenko, E., Dzestelova, A., Mikhailova, E., Malykh, V., Savelev, A., & Motyko, A. A. (2024) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct64283.2024.10749962 · Cited by 10
Research on Text to Image Based on Generative Adversarial Network
Li, X., & Gao, Y. (2020) · International Conference on Information Technology and Computer Application (ITCA) · doi:10.1109/itca52113.2020.00077 · Cited by 9
Stereo Waterdrop Removal with Row-wise Dilated Attention
Shi, Z., Fan, N., Yeung, D., & Chen, Q. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636216 · Cited by 9
Self-citationPhotorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis With Rendering
Yurtsever, E., Yang, D., Koc, I. M., & Redmill, K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3193347 · Cited by 9
Edge Aware Domain Transformation for Face Sketch Synthesis
Zhang, C., Liu, D., Peng, C., Wang, N., & Gao, X. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3195383 · Cited by 9
Deepfakes on Retinal Images using GAN
Salini, Y., & Harikiran, J. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130880 · Cited by 9
Free-Viewpoint RGB-D Human Performance Capture and Rendering
Nguyen-Ha, P., Sarafianos, N., Lassner, C., Heikkilä, J., & Tung, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19787-1_27 · Cited by 9
Lin, C.-S., & Wang, Y.-C. F. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3268001 · Cited by 9
Unsupervised image-to-image translation in multi-parametric MRI of bladder cancer
Chen, Z., Cai, L., Chen, C., Fu, X. G., Yang, X., Yuan, B., Lü, Q., & Zhou, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106547 · Cited by 9
Two‐phase Hair Image Synthesis by Self‐Enhancing Generative Model
Qiu, H., Wang, C., Zhu, H., Zhu, X., Gu, J., & Han, X. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13847 · Cited by 9
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Stimpel, B., Syben, C., Würfl, T., Breininger, K., Hoelter, P., Dörfler, A., & Maier, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-55108-8 · Cited by 9
Aesthetic-Aware Text to Image Synthesis
Baraheem, S. S., & Nguyen, T. (2020) · Conference on Information Sciences and Systems · doi:10.1109/ciss48834.2020.1570617383 · Cited by 9
Mix and Match Networks: Cross-Modal Alignment for Zero-Pair Image-to-Image Translation
Wang, Y., Herranz, L., & van de Weijer, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01340-z · Cited by 10
Cruz, N., & Ruíz-del-Solar, J. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207173 · Cited by 9
Hwang, S., Jeon, S., Ma, Y., & Byun, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12934-9 · Cited by 9
Qi, Y., Su, G., Wang, Q., Yang, J., Pang, K., & Song, Y.-Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01623-7 · Cited by 9
Automated filter selection for suppression of Gibbs ringing artefacts in MRI
Wang, Y., & Healy, J. J. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.07.007 · Cited by 9
On the Diversity of Conditional Image Synthesis With Semantic Layouts
Yang, Z., Liu, H., & Cai, D. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2891935 · Cited by 9
Multi-Process Training GAN for Identity-Preserving Face Synthesis
Tang, Z., Yang, J., Pei, Z., Song, X., & Ge, B. (2019) · IEEE Access · doi:10.1109/access.2019.2930203 · Cited by 9
Cycle-Consistent Diverse Image Synthesis from Natural Language
Chen, Z., & Luo, Y. (2019) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2019.00085 · Cited by 9
Tian, A., & Lu, L. (2020) · IEEE Access · doi:10.1109/access.2020.2964946 · Cited by 11
Text-to-image via mask anchor points
Baraheem, S. S., & Nguyen, T. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.02.013 · Cited by 9
InverseNet: Solving Inverse Problems with Splitting NetworksPreprint
Fan, K., Wei, Q., Wang, W., Chakraborty, A., & Heller, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00202 · Cited by 9
UPR-Net: A Unified Pyramid Recurrent Network for Video Frame Interpolation
Jin, X., Wu, L., Chen, J., Chen, Y., Koo, J., Hahm, C.-H., & Chen, Z.-M. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02164-x · Cited by 8
Joint residual pyramid for joint image super-resolution
Zheng, Y., Cao, X. G., Xiao, Y., Zhu, X., & Yuan, J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.11.028 · Cited by 8
Ullah, A., Numan, M., Khalid, M. N. A., & Majid, A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129767 · Cited by 10
Semanticgan: Generative Adversarial Networks For Semantic Image To Photo-Realistic Image Translation
Liu, J., Zou, Y., & Yang, D. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053087 · Cited by 8
Thermal Face Recognition Based on Multi-scale Image Synthesis
Chu, W.-T., & Huang, P.-S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67832-6_9 · Cited by 8
A Deep Learning Approach to Mobile Camera Image Signal Processing
Silva, J. I. S., Carvalho, G. G., Santos, M. S., Santiago, D. J. C., de Albuquerque, L. P., de Battle, J., Costa, G. M. D., & Ren, T. I. (2020) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2020.13016 · Cited by 8
Transfer Deep Learning for Reconfigurable Snapshot HDR Imaging Using Coded Masks
Alghamdi, M., Fu, Q., Thabet, A., & Heidrich, W. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14205 · Cited by 9
Stacked generative adversarial networks for image compositing
Yu, B., Ding, Y., Xie, Z., & Huang, D. (2021) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-021-00550-w · Cited by 9
Video prediction: a step-by-step improvement of a video synthesis network
Jing, B., Ding, H., Yang, Z., Li, B., & Bao, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02500-5 · Cited by 8
Mallat, K., & Dugelay, J. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00159 · Cited by 8
Image generation step by step: animation generation-image translation
Jing, B., Ding, H., Yang, Z., Li, B. H., & Liu, Q. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02835-z · Cited by 8
Part-Preserving Pose Manipulation for Person Image Synthesis
Dong, H., Liang, X., Zhou, C., Lai, H., Zhu, J., & Yin, J. P. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00215 · Cited by 8
Ananthabhotla, I., Ewert, S., & Paradiso, J. A. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351148 · Cited by 8
Image Inpainting Guided by Coherence Priors of Semantics and TexturesPreprint
Liao, L., Xiao, J., Wang, Z., Lin, C., & Satoh, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.08054 · Cited by 8
Image-to-image Translation via Hierarchical Style DisentanglementPreprint
Li, X., Zhang, S., Hu, J., Cao, L., Hong, X., Mao, X., Huang, F., Wu, Y., & Ji, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.01456 · Cited by 8
Few-shot Semantic Image Synthesis Using StyleGAN PriorPreprint
Endo, Y., & Kanamori, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14877 · Cited by 8
Lin, J., Zhao, W., & Wang, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3358027 · Cited by 7
Fime, A. A., Mahmud, S., Das, A., Islam, M. K., & Kim, J.-H. (2025) · IEEE Access · doi:10.1109/access.2025.3574298 · Cited by 12
Decomposing Image Generation into Layout Prediction and Conditional Synthesis
Volokitin, A., Ender Konukoglu, A., & Gool, L. V. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00194 · Cited by 7
View synthesis with multiplane images from computationally generated RGB-D light fields
Yoon, G.-J., Jung, G., Song, J., & Yoon, S. M. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107930 · Cited by 7
Sweat Gland Enhancement Method for Fingertip OCT Images Based on Generative Adversarial Network
Miao, Q., Wang, H., Zhang, Y., Yan, R., & Liu, Y. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3459812 · Cited by 7
Constrained Layout Generation with Factor Graphs
Dupty, M. H., Dong, Y., Leng, S., Fu, G., Goh, Y. L., Lu, W., & Lee, W. S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01221 · Cited by 7
Photographic image synthesis with improved U-net
Liu, G., Si, J., Hu, Y., & Li, S. (2018) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci.2018.8377492 · Cited by 7
Towards a Throughput and Energy Efficient Association Strategy for Wi-Fi/LiFi Heterogeneous Networks
Guérin, E., Begin, T., Busson, A., & Lassous, I. G. (2021) · ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks (PE-WASUN) · doi:10.1145/3479240.3488514 · Cited by 8
Learning To Compose 6-DOF Omnidirectional Videos Using Multi-Sphere Images
Li, J., He, Y., Hu, Y., Han, Y., & Wen, J. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506127 · Cited by 7
Discriminative Region Proposal Adversarial Network for High-Quality Image-to-Image Translation
Wang, C., Niu, W., Jiang, Y., Zheng, H., Yu, Z., Gu, Z., & Zheng, B. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01273-2 · Cited by 8
Effective Free-Driving Region Detection for Mobile Robots by Uncertainty Estimation Using RGB-D Data
Nguyen, T.-K., Nguyen, P. T.-T., Nguyen, D.-D., & Kuo, C. (2022) · Sensors · doi:10.3390/s22134751 · Cited by 8
Multi-Instance Sketch to Image Synthesis With Progressive Generative Adversarial Networks
Wang, Z., Wang, N., Shi, J., Li, J., & Yang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2913178 · Cited by 7
Multi-Scale Network with the Deeper and Wider Residual Block for MRI Motion Artifact Correction
Zhang, W., Zhang, Q.-Y., Yang, J., & Wang, Q. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.10240 · Cited by 7
Realistic post-processing of rendered 3D scenes
Feygina, A., Ignatov, D. I., & Makarov, I. (2018) · ACM SIGGRAPH Posters · doi:10.1145/3230744.3230764 · Cited by 7
Lahiri, A., Jain, A., Biswas, P. K., & Mitra, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06106 · Cited by 7
Learning Representations for Predicting Future ActivitiesPreprint
Zolfaghari, M., Çiçek, Ö., Ali, S. M., Mahdisoltani, F., Zhang, C., & Brox, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.03578 · Cited by 7
Automatically Searching for U-Net Image Translator ArchitecturePreprint
Shu, H., & Wang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11581 · Cited by 9
SCP-Diff: Spatial-Categorical Joint Prior for Diffusion Based Semantic Image Synthesis
Gao, H.-A., Gao, M., Li, J., Li, W., Zhi, R., Tang, H., & Zhao, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_3 · Cited by 6
Exploring the Advancements and Challenges of Deepfake Face-swap: A Survey
Bakar, S. A. R. B. S. A., Waseem, S., Omar, Z., & Bilalashfaqahmed (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21285-8 · Cited by 13
Realistic image generation using adversarial generative networks combined with depth information
Qi, Y., Yu, L., Li, G., Jin, D., & Qi, M. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104263 · Cited by 6
Du, X., Peng, J., Zhou, Y., Zhang, J., Chen, S., Jiang, G., Sun, X., & Ji, R. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612067 · Cited by 8
SegTex: A Large Scale Synthetic Face Dataset for Face Recognition
Ambardi, L., Hong, S., & Park, I. K. (2023) · IEEE Access · doi:10.1109/access.2023.3336405 · Cited by 7
Semantic GAN: Application for Cross-Domain Image Style Transfer
Li, P., & Yang, M. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00161 · Cited by 6
Semi-Supervised Landmark-Guided Restoration of Atmospheric Turbulent Images
Lau, C. P., Kumar, A., & Chellappa, R. (2021) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2021.3050979 · Cited by 6
Modelling spatio-temporal ageing phenomena with deep Generative Adversarial Networks
Papadopoulos, S., Dimitriou, N., Drosou, A., & Tzovaras, D. K. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116200 · Cited by 6
Multi-Style Unsupervised Image Synthesis Using Generative Adversarial Nets
Lv, G., Israr, S. M., & Qi, S. (2021) · IEEE Access · doi:10.1109/access.2021.3087665 · Cited by 6
Qi, S., Sang, X., Yan, B., Wang, P., Chen, D., Wang, H., Ye, X., & Wan, H. (2022) · Optics Communications · doi:10.1016/j.optcom.2022.128679 · Cited by 6
Image Generation from Scene Graph with Object Edges
Li, C., Duan, Y., Du, Q., Pan, C., Liu, G., & Tao, X. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-fall57202.2022.10012878 · Cited by 6
Improved Image Synthesis with Attention Mechanism for Virtual Scenes via UAV Imagery
Mo, L., Zhu, Y., Wang, G., Yi, X., Wu, X., & Wu, P. (2023) · Drones · doi:10.3390/drones7030160 · Cited by 6
Dual conditional GAN based on external attention for semantic image synthesis
Liu, G., Zhou, Q., Xie, X., & Yu, Q. (2023) · Connection Science · doi:10.1080/09540091.2023.2259120 · Cited by 6
Diversifying Semantic Image Synthesis and Editing via Class‐ and Layer‐wise VAEs
Endo, Y., & Kanamori, Y. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14164 · Cited by 6
Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization
Lee, H.-Y., Kim, N., Lee, J., & Lee, B. (2021) · IEEE Access · doi:10.1109/access.2021.3091980 · Cited by 6
A Shared Representation for Photorealistic Driving Simulators
Saadatnejad, S., Li, S., Mordan, T., & Alahi, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3131303 · Cited by 7
Deformable Geometry based Semantic Reconstruction from Scene Graphs
Wang, Z., Li, Y., Huang, D., Luo, Y., Ge, N., & Lü, J. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685640 · Cited by 6
Virtual view synthesis for 3D light-field display based on feature reprojection and fusion
Qi, S., Sang, X., Yan, B., Chen, D., Wang, P., Wang, H., & Ye, X. (2022) · Optics Communications · doi:10.1016/j.optcom.2022.128383 · Cited by 6
Zhao, L., Lin, S., Li, A., Lin, H., Xing, W., & Lu, D. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413760 · Cited by 7
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition NetworkPreprint
Guan, H., Liu, L., Moran, S., Song, F., & Slabaugh, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05249 · Cited by 6
Seibold, C., Fink, M. A., Goos, C., Kauczor, H., Schlemmer, H., Stiefelhagen, R., & Kleesiek, J. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433966
Chikwendu, I. A., Zhang, X., Monday, H. N., Nneji, G. U., Ukwuoma, C. C., Chikwendu, O. C., Gu, Y. H., & Al–antari, M. A. (2025) · Electronics · doi:10.3390/electronics14061158 · Cited by 5
Joint Residual Pyramid for Depth Map Super-Resolution
Xiao, Y., Cao, X. G., Zheng, Y., & Zhu, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-97304-3_61 · Cited by 5
Pose-Guided Person Image Synthesis for Data Augmentation in Pedestrian Detection
Zhi, R., Guo, Z., Zhang, W., Wang, B., Kaiser, V., Wiederer, J., & Flohr, F. B. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575574 · Cited by 5
Toward multi-category garments virtual try-on method by coarse to fine TPS deformation
Fang, N., Qiu, L., Zhang, S., Wang, Z., Hu, K., & Li, H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07173-w · Cited by 5
Liu, D., Shetty, S., Hinz, T., Fisher, M., Zhang, R., Park, T., & Kalogerakis, E. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530172 · Cited by 10
Semantic inpainting on segmentation map via multi-expansion loss
He, J., Zhang, X., Lei, S., Wang, S., Lu, C., & Xiao, B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.025 · Cited by 5
A multi-source feature extraction network for salient object detection
Xu, K., & Guo, J. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-08172-7 · Cited by 5
DR2: Disentangled Recurrent Representation Learning for Data-efficient Speech Video Synthesis
Zhang, C., Wang, C., Zhao, Y., Cheng, S., Luo, L., & Guo, X. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00609 · Cited by 6
Learning View Synthesis for Desktop Telepresence With Few RGBD Cameras
Wang, S., Wang, Z., Schmelzle, R., Zheng, L., Kwon, Y., Sengupta, R., & Fuchs, H. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3411626 · Cited by 5
Jung, G., Lee, J., & Kim, S. (2024) · Biomedical Engineering Letters · doi:10.1007/s13534-024-00409-9 · Cited by 5
Mallat, K., Damer, N., Boutros, F., & Dugelay, J. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011432 · Cited by 5
H-GAN: the power of GANs in your Hands
Oprea, S., Karvounas, G., Martínez-González, P., Kyriazis, N., Orts‐Escolano, S., Oikonomidis, I., García-García, A., Tsoli, A., García‐Rodríguez, J., & Argyros, A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534144 · Cited by 5
Improved Deep Distributed Light Field Coding
Mukati, M. U., Stepanov, M., Valenzise, G., Forchhammer, S. O., & Frederic Dufaux, A. (2021) · IEEE Open Journal of Circuits and Systems · doi:10.1109/ojcas.2021.3073252 · Cited by 5
Image to Image Translation Networks using Perceptual Adversarial Loss Function
Altakrouri, S., Usman, S., Ahmad, N. B., Justinia, T., & Noor, N. M. (2021) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa52582.2021.9576815 · Cited by 5
Learning Robust Image-Based Rendering on Sparse Scene Geometry via Depth Completion
Sun, Y., Zhou, S., Cheng, R., Tan, W., Yan, B., & Fu, L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00766 · Cited by 5
Zero-Shot Learning with Joint Generative Adversarial Networks
Zhang, M., Wang, X., Shi, Y., Ren, S., & Wang, W. (2023) · Electronics · doi:10.3390/electronics12102308 · Cited by 6
Attention guided domain alignment for conditional face image generation
Li, Z., Zhang, S., Zhang, Z., Meng, Q., Liu, Q., & Zhou, H. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103740 · Cited by 5
Metadata
- Authors
- Chen, Q., & Koltun, V.
- Affiliations
- Intel (United States) (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1520-1529
- OpenAlex
- W2963522749
- Cited by
- 887
- References
- 62
- Citations by year
- 2026: 13 · 2025: 40 · 2024: 51 · 2023: 88 · 2022: 117 · 2021: 170 · 2020: 145 · 2019: 156 · 2018: 94 · 2017: 12
- FWCI
- 32.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- semantic segmentation, feedforward neural networks, high-resolution image generation, realistic image synthesis, outdoor scenes, indoor scenes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
