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 301–400 of 886
Manipulating Attributes of Natural Scenes via Hallucination
Karacan, L., Akata, Z., Erdem, A., & Erdem, E. (2019) · ACM Transactions on Graphics · doi:10.1145/3368312 · Cited by 29
Kamran, S. A., Hossain, K. F., Tavakkoli, A., & Zuckerbrod, S. L. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412428 · Cited by 29
Damer, N., Boutros, F., Saladié, A. M., Kirchbuchner, F., & Kuijper, A. (2019) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185994 · Cited by 30
Contrastive Feature Loss for Image Prediction
Andonian, A., Park, T., Russell, B. C., Isola, P., Zhu, J.-Y., & Zhang, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00220 · Cited by 30
Edge Guided GANs with Semantic Preserving for Semantic Image SynthesisPreprint
Tang, H., Qi, X., Xu, D., Torr, P. H. S., & Sebe, N. (2020) · arXiv (Cornell University) · Cited by 29
High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks
Guo, B., Geng, G., Zhu, L., Shi, H., & Yu, Z. (2019) · Sensors · doi:10.3390/s19143075 · Cited by 28
Unsupervised Portrait Shadow Removal via Generative Priors
He, Y., Xing, Y., Zhang, T., & Chen, Q. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475663 · Cited by 28
Self-citationChen, W.-X., Li, Y., & Zhao, Z. (2021) · Remote Sensing · doi:10.3390/rs13193971 · Cited by 30
ChebyLighter: Optimal Curve Estimation for Low-light Image Enhancement
Pan, J., Zhai, D., Bai, Y., Jiang, J., Zhao, D., & Liu, X. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548135 · Cited by 28
Multi-feature contrastive learning for unpaired image-to-image translation
Gou, Y., Li, M., Song, Y. C., He, Y., & Wang, L. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00924-1 · Cited by 29
Cai, S., Chan, E. R., Peng, S., Shahbazi, M., Obukhov, A., Gool, L. V., & Wetzstein, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00204 · Cited by 43
High-Resolution Image Dehazing with Respect to Training Losses and Receptive Field Sizes
Sim, H., Ki, S., Choi, J.-S., Kim, S. Y., Seo, S., Kim, S., & Kim, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00136 · Cited by 28
SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation
Xu, W., Li, Y., & Lu, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_8 · Cited by 28
Image Manipulation with Perceptual Discriminators
Sungatullina, D., Zakharov, E., Ulyanov, D., & Lempitsky, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_36 · Cited by 27
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks
Huh, M., Sun, S.-H., & Zhang, N. (2019) · 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/cvpr.2019.00157 · Cited by 28
Kamran, S. A., Hossain, K. F., Tavakkoli, A., Zuckerbrod, S. L., Baker, S. A., & Sanders, K. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64559-5_10 · Cited by 27
Dai, X., Yuan, X., & Wei, X. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02445-9 · Cited by 27
Local and Global GANs With Semantic-Aware Upsampling for Image Generation
Tang, H., Shao, L., Torr, P. H. S., & Sebe, N. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3155989 · Cited by 27
Look-Ahead Training with Learned Reflectance Loss for Single-Image SVBRDF Estimation
Zhou, X., & Kalantari, N. K. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555495 · Cited by 27
Modeling and Simulation of Sidescan Using Conditional Generative Adversarial Network
Bore, N., & Folkesson, J. B. (2020) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2020.2980456 · Cited by 27
Geometric-Pixel Guided Single-Pass Convolution Neural Network With Graph Cut for Image Dehazing
Alenezi, F. S., & Ganesan, S. (2021) · IEEE Access · doi:10.1109/access.2021.3059115 · Cited by 26
Fashion Customization: Image Generation Based on Editing Clue
Song, D., Zeng, J., Liu, M., Li, X., & Liu, A.-A. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3338459 · Cited by 26
Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation
Gao, F., Xu, X., Yu, J., Shang, M., Li, X., & Tao, D. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3061286 · Cited by 26
Nham, D.-H.-N., Trinh, M.-N., Nguyen, V.-D., Pham, V.-T., & Tran, T.-T. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104631 · Cited by 27
Liu, S., Zhang, X., Wangni, J., & Shi, J. (2019) · 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/cvpr.2019.01055 · Cited by 26
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image SynthesisPreprint
Zhang, Z., Ma, J., Zhou, C., Men, R., Li, Z., Ding, M., Tang, J., Zhou, J., & Yang, H. (2021) · arXiv (Cornell University) · Cited by 26
Fast User-Guided Single Image Reflection Removal via Edge-Aware Cascaded Networks
Zhang, H., Xu, X., He, H., He, S., Han, G., Qin, J., & Wu, D. O. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2951461 · Cited by 25
View Synthesis of Dynamic Scenes Based on Deep 3D Mask Volume
Lin, K., Yang, G., Xiao, L., Liu, F., & Ramamoorthi, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3289333 · Cited by 24
Learning Dynamic Textures for Neural Rendering of Human Actors
Liu, L., Xu, W., Habermann, M., Zollhöfer, M., Bernard, F., Kim, H., Wang, W., & Theobalt, C. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.2996594 · Cited by 71
Semantic-Driven Face Hallucination Based on Residual Network
Yu, X., Zhang, L., & Xie, W. C. (2021) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2021.3051268 · Cited by 23
Deep image synthesis from intuitive user input: A review and perspectives
Xue, Y., Guo, Y.-C., Zhang, H., Xu, T., Zhang, S., & Huang, X. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0234-8 · Cited by 24
Label-Guided Generative Adversarial Network for Realistic Image Synthesis
Zhu, J., Gao, L., Song, J., Li, Y.-F., Zheng, F., Li, X., & Shen, H. T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3186752 · Cited by 26
Yu, Z., Chen, K., Li, S., Han, B., Liu, C. H., & Wang, S. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548221 · Cited by 24
VISMA: A Machine Learning Approach to Image Manipulation
Bende, M., Khandelwal, M., Borgaonkar, D., & Khobragade, P. (2023) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon57294.2023.10112168 · Cited by 24
UID-GAN: Unsupervised Image Deblurring via Disentangled Representations
Lu, B., Chen, J.-C., & Chellappa, R. (2019) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2019.2959133 · Cited by 24
Yang, Z., Wang, T., Bu, L., & Ouyang, J. (2021) · Fire Technology · doi:10.1007/s10694-021-01117-x · Cited by 24
3D car shape reconstruction from a contour sketch using GAN and lazy learning
Nozawa, N., Shum, H. P. H., Feng, Q., Ho, E. S. L., & Morishima, S. (2021) · The Visual Computer · doi:10.1007/s00371-020-02024-y · Cited by 23
Fake visual content detection using two-stream convolutional neural networks
Yousaf, B., Usama, M., Sultani, W., Mahmood, A., & Qadir, J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06902-5 · Cited by 23
Luo, Y., Lü, J., Jiang, X., & Zhang, B. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3056384 · Cited by 23
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
Zhou, Y., Wu, G., Fu, Y., Li, K., & Liu, Y. (2021) · 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/cvpr46437.2021.01460 · Cited by 23
Composition-Aware Image Steganography Through Adversarial Self-Generated Supervision
Zheng, Z., Hu, Y., Bin, Y., Xu, X., Yang, Y., & Shen, H. T. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3175627 · Cited by 25
Benefiting from multitask learning to improve single image super-resolution
Rad, M. S., Bozorgtabar, B., Musat, C., Marti, U.-V., Basler, M., Ekenel, H. K., & Thiran, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.107 · Cited by 23
Neural Contrast Enhancement of CT Image
Seo, M., Kim, D.-K., Lee, K., Hong, S., Bae, J. S., Kim, J. H., & Kwak, S. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00402 · Cited by 23
High-resolution Deep Convolutional Generative Adversarial NetworksPreprint
de Curtò, J., de Zarzà, I., la Torre, F. D., King, I., & Lyu, M. R.-T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06491 · Cited by 23
Yan, Z., Wen, F., Ying, R., Ma, C., & Liu, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02782 · Cited by 22
Facial Synthesis From Visual Attributes via Sketch Using Multiscale Generators
Di, X., & Patel, V. M. (2019) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2019.2961926 · Cited by 22
SignSynth: Data-Driven Sign Language Video Generation
Stoll, S., Hadfield, S., & Bowden, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66823-5_21 · Cited by 22
Xia, W., Yang, Y., & Xue, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.029 · Cited by 22
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation
Lambert, J., Liu, Z., Şener, O., Hays, J. H., & Koltun, V. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3151200 · Cited by 24
Self-citationEdge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis
Tang, H., Sun, G.-L., Sebe, N., & van Gool, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3298721 · Cited by 26
PLACE: Adaptive Layout-Semantic Fusion for Semantic Image Synthesis
Lv, Z., Wei, Y., Zuo, W., & Wong, K.-Y. K. (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/cvpr52733.2024.00885 · Cited by 27
Multi-Level Encoder-Decoder Architectures for Image Restoration
Mastan, I. D., & Raman, S. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00223 · Cited by 22
Deep Multi-Scale Recurrent Network for Synthetic Aperture Radar Images Despeckling
Zhou, Y., Shi, J., Yang, X., Wang, C., Kumar, D., Wei, S., & Zhang, X. (2019) · Remote Sensing · doi:10.3390/rs11212462 · Cited by 21
Visual-Relation Conscious Image Generation from Structured-Text
Vo, D. M., & Sugimoto, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_18 · Cited by 22
Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current ApproachesPreprint
Seib, V., Lange, B., & Wirtz, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08781 · Cited by 34
Vid2Actor: Free-viewpoint Animatable Person Synthesis from Video in the WildPreprint
Weng, C.-Y., Curless, B., & Kemelmacher-Shlizerman, I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12884 · Cited by 24
DanbooRegion: An Illustration Region Dataset
Zhang, L., Ji, Y., & Liu, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_9 · Cited by 21
LFIEM: Lightweight Filter-based Image Enhancement Model
Tatanov, O., & Samarin, A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413138 · Cited by 21
Face deblurring using dual camera fusion on mobile phones
Lai, W., Shih, Y., Chu, L.-C., Wu, X., Tsai, S.-F., Krainin, M., Sun, D., & Liang, C. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530131 · Cited by 21
PixelNN: Example-based Image SynthesisPreprint
Bansal, A., Sheikh, Y. A., & Ramanan, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05349 · Cited by 21
Shapes and Context: In-The-Wild Image Synthesis & Manipulation
Bansal, A., Sheikh, Y. A., & Ramanan, D. (2019) · 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/cvpr.2019.00242 · Cited by 20
Toward Multi-Modal Conditioned Fashion Image Translation
Gu, X., Yu, J., Wong, Y., & Kankanhalli, M. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3009500 · Cited by 20
A Survey on the Development of Image Data Augmentation
冯, 晓. (2021) · Computer Science and Application · doi:10.12677/csa.2021.112037 · Cited by 20
Chen, J., Zhu, F., Han, Y., & Chen, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115727 · Cited by 20
Instant Automatic Emptying of Panoramic Indoor Scenes
Pintore, G., Agus, M., Almansa, E., & Gobbetti, E. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3202999 · Cited by 20
Image Generation with Gans-based Techniques: A Survey
Esfahani, S. N., & Latifi, S. (2019) · International Journal of Computer Science and Information Technology · doi:10.5121/ijcsit.2019.11503 · Cited by 20
Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy
Han, C., Duan, Y., Tao, X., Xu, M., & Lü, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2965055 · Cited by 20
Sketch-guided Deep Portrait Generation
Ho, T.-T., Virtusio, J. J., Chen, Y.-Y., Hsu, C.-M., & Hua, K. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3396237 · Cited by 21
Layout-to-Image Translation With Double Pooling Generative Adversarial Networks
Tang, H., & Sebe, N. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3109531 · Cited by 20
Compositional Transformers for Scene GenerationPreprint
Hudson, D. A., & Zitnick, C. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08960 · Cited by 20
Generating Manga from Illustrations via Mimicking Manga Creation Workflow
Zhang, L., Wang, X., Fan, Q., Ji, Y., & Liu, C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00559 · Cited by 20
SwinIT: Hierarchical Image-to-Image Translation Framework Without Cycle Consistency
Liu, J., Fu, H., Wang, X., & Ma, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3353932 · Cited by 20
D 3 : Scaling Up Deepfake Detection by Learning from Discrepancy
Yang, Y., Qian, Z., Zhu, Y., Russakovsky, O., & Wu, Y. (2025) · 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/cvpr52734.2025.02221 · Cited by 39
NAM: Non-Adversarial Unsupervised Domain Mapping
Hoshen, Y., & Wolf, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_27 · Cited by 19
End-to-End Text-to-Image Synthesis with Spatial Constrains
Wang, M., Lang, C., Liang, L., Feng, S., Wang, T., & Gao, Y. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3391709 · Cited by 19
Qiao, Y., Chen, Q., Deng, C., Ding, N., Qi, Y., Tan, M., Ren, X., & Wu, Q. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475363 · Cited by 19
Self-citationImage generation models from scene graphs and layouts: A comparative analysis
Hassan, M. U., Alaliyat, S., & Hameed, I. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.03.021 · Cited by 19
Learning Generative Models of Tissue Organization with Supervised GANs
Han, L., Murphy, R. F., & Ramanan, D. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00080 · Cited by 19
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
Li, K., Peng, S., Zhang, T., & Malik, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01325-y · Cited by 22
Controllable Multi-Attribute Editing of High-Resolution Face Images
Deng, Q., Li, Q., Cao, J., Liu, Y.-F., & Sun, Z.-N. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3033184 · Cited by 16
Dai, L., & Tang, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3062849 · Cited by 19
A-PixelHop: A Green, Robust and Explainable Fake-Image Detector
Zhu, Y. C., Wang, X., Chen, H.-S., Salloum, R., & Kuo, C. ‐. J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747901 · Cited by 19
Wang, J., Lu, G., Xu, H., Li, Z., Xu, C., & Fu, Y. (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/cvpr52688.2022.01044 · Cited by 21
Deep Perceptual CompressionPreprint
Patel, Y., Appalaraju, S., & Manmatha, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08310 · Cited by 19
Semantic Bottleneck Scene GenerationPreprint
Azadi, S., Tschannen, M., Tzeng, E., Gelly, S., Darrell, T., & Lučić, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11357 · Cited by 19
Example-Guided Image Synthesis Using Masked Spatial-Channel Attention and Self-supervision
Zheng, H., Liao, H., Chen, L., Xiong, W., Chen, T., & Luo, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_25 · Cited by 18
L2R GAN: LiDAR-to-Radar Translation
Wang, L., Goldlüecke, B., & Anklam, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69535-4_43 · Cited by 18
Network Bending: Expressive Manipulation of Deep Generative Models
Broad, T., Leymarie, F. F., & Grierson, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72914-1_2 · Cited by 18
Recent trending on learning based video compression: A survey
Hoang, T. M., & Zhou, J. (2021) · Cognitive Robotics · doi:10.1016/j.cogr.2021.08.003 · Cited by 18
FLNet: Graph Constrained Floor Layout Generation
Upadhyay, A., Dubey, A., Arora, V., Kuriakose, S. M., & Agarawal, S. (2022) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew56448.2022.9859350 · Cited by 18
A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint
Ullah, U., Lee, J.-S., An, C.-H., Lee, H., Park, S.-Y., Baek, R., & Choi, H. (2022) · Sensors · doi:10.3390/s22186816 · Cited by 19
Zhang, Z., & Ding, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548330 · Cited by 18
Image-Consistent Detection of Road Anomalies as Unpredictable Patches
Vojir, T., & Matas, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00545 · Cited by 20
MIDMs: Matching Interleaved Diffusion Models for Exemplar-Based Image Translation
Seo, J., Lee, G., Cho, S., Lee, J., & Kim, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25313 · Cited by 28
MS-GAN: multi-scale GAN with parallel class activation maps for image reconstruction
Rao, J., Ke, A., Liu, G., & Ming, Y. (2022) · The Visual Computer · doi:10.1007/s00371-022-02468-4 · Cited by 18
DeepView: View Synthesis with Learned Gradient DescentPreprint
Flynn, J., Broxton, M. J., Debevec, P. E., DuVall, M., Fyffe, G., Overbeck, R., Snavely, N., & Tucker, R. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07316 · Cited by 18
Text and Style Conditioned GAN for Generation of Offline Handwriting LinesPreprint
Davis, B., Tensmeyer, C., Price, B. L., Wigington, C., Morse, B. S., & Jain, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.00678 · Cited by 18
Liu, C.-H., Chang, H., & Park, T. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00257 · Cited by 17
Scene Graph tO Image Generation with Contextualized Object Layout Refinement
Ivgi, M., Benny, Y., David, A. B., Berant, J., & Wolf, L. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506651 · Cited by 17
Tyagi, S., & Yadav, D. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09672-w · Cited by 18
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
