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Visual scene graph generation is a challenging task.
J. Gu, H. Zhao, Z. Lin, S. Li, J. Cai, and M. Ling, “Scene graph generation with external knowledge and image reconstruction,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 2019, pp. 1969–1978
1978
Earlier work this paper cites.
Y. Cao, J. Xu, S. Lin, F. Wei, and H. Hu, “Gcnet: Non-local networks meet squeeze-excitation networks and beyond,” in 2019 IEEE/CVF International Conference on Computer Vision Workshops, ICCV Workshops 2019, Seoul, Korea (South), October 27-28, 2019 . IEEE, 2019, pp. 1971–1980
1980
Earlier work this paper cites.
J. Deng, N. Ding, Y. Jia, A. Frome, K. Murphy, S. Bengio, Y. Li, H. Neven, and H. Adam, “Large-scale object classification using label relation graphs,” in Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I , 2014, pp. 48–64
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015 , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
J. Johnson, R. Krishna, M. Stark, L. Li, D. A. Shamma, M. S. Bernstein, and F. Li, “Image retrieval using scene graphs,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015 , 2015, pp. 3668–3678
2015
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C. Berg, “SSD: single shot multibox detector,” in Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I , 2016, pp. 21–37
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 , 2016, pp. 770–778
2016
Earlier work this paper cites.
C. Xiong, S. Merity, and R. Socher, “Dynamic memory networks for visual and textual question answering,” in Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 , 2016, pp. 2397–2406
2016
Earlier work this paper cites.
C. Lu, R. Krishna, M. S. Bernstein, and F. Li, “Visual relationship detection with language priors,” in Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I , 2016, pp. 852–869
2016
Earlier work this paper cites.
C. Huang, Y. Li, C. C. Loy, and X. Tang, “Learning deep representation for imbalanced classification,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 , 2016, pp. 5375–5384
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel, “Gated graph sequence neural networks,” in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , 2016
2016
Earlier work this paper cites.
A. Shrivastava, A. Gupta, and R. B. Girshick, “Training region-based object detectors with online hard example mining,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 , 2016, pp. 761–769
2016
Earlier work this paper cites.
J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017, pp. 6517–6525
2017
Earlier work this paper cites.
Y. Li, W. Ouyang, X. Wang, and X. Tang, “Vip-cnn: Visual phrase guided convolutional neural network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017, pp. 7244–7253
2017
Earlier work this paper cites.
D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei, “Scene graph generation by iterative message passing,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017, pp. 3097–3106
2017
Earlier work this paper cites.
H. Zhang, Z. Kyaw, S. Chang, and T. Chua, “Visual translation embedding network for visual relation detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017, pp. 3107–3115
2017
Earlier work this paper cites.
J. Zhang, M. Elhoseiny, S. Cohen, W. Chang, and A. M. Elgammal, “Relationship proposal networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017, pp. 5226–5234
2017
Cited alongside, same era.
B. Zhuang, L. Liu, C. Shen, and I. D. Reid, “Towards context-aware interaction recognition for visual relationship detection,” in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017 , 2017, pp. 589–598
2017
Cited alongside, same era.
Y. Li, W. Ouyang, B. Zhou, K. Wang, and X. Wang, “Scene graph generation from objects, phrases and region captions,” in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017 . IEEE Computer Society, 2017, pp. 1270–1279
2017
Cited alongside, same era.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L. Li, D. A. Shamma, M. S. Bernstein, and L. Fei-Fei, “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” International Journal of Computer Vision , vol. 123, no. 1, pp. 32–73, 2017
Q. Li, Z. Han, and X. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 , 2018, pp. 3538–3545
2018
Later among the works it cites.
Y. Zhang, J. S. Hare, and A. Prügel-Bennett, “Learning to count objects in natural images for visual question answering,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, 2018
2018
Later among the works it cites.
K. Tang, H. Zhang, B. Wu, W. Luo, and W. Liu, “Learning to compose dynamic tree structures for visual contexts,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 2019, pp. 6619–6628
2019
Later among the works it cites.
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2017
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , 2017
2017
Cited alongside, same era.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 6, pp. 1137–1149, 2017
2017
Cited alongside, same era.
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 4, pp. 834–848, 2018
2018
Cited alongside, same era.
J. Gu, S. R. Joty, J. Cai, and G. Wang, “Unpaired image captioning by language pivoting,” in Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I , 2018, pp. 519–535
2018
Cited alongside, same era.
Q. Wu, C. Shen, P. Wang, A. R. Dick, and A. van den Hengel, “Image captioning and visual question answering based on attributes and external knowledge,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 6, pp. 1367–1381, 2018
2018
Cited alongside, same era.
B. Wu, F. Jia, W. Liu, B. Ghanem, and S. Lyu, “Multi-label learning with missing labels using mixed dependency graphs,” International Journal of Computer Vision , vol. 126, no. 8, pp. 875–896, 2018
2018
Cited alongside, same era.
J. Johnson, A. Gupta, and L. Fei-Fei, “Image generation from scene graphs,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018 , 2018, pp. 1219–1228
2018
Cited alongside, same era.
Y. Li, W. Ouyang, B. Zhou, J. Shi, C. Zhang, and X. Wang, “Factorizable net: An efficient subgraph-based framework for scene graph generation,” in Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I , 2018, pp. 346–363
2018
Cited alongside, same era.
2019
Later among the works it cites.
T. Chen, W. Yu, R. Chen, and L. Lin, “Knowledge-embedded routing network for scene graph generation,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 2019, pp. 6163–6171
2019
Later among the works it cites.
L. Shi, Y. Zhang, J. Cheng, and H. Lu, “Two-stream adaptive graph convolutional networks for skeleton-based action recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 2019, pp. 12 026–12 035
2019
Later among the works it cites.
J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, “Libra R-CNN: towards balanced learning for object detection,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 2019, pp. 821–830
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Rahimi, T. Cohn, and T. Baldwin, “Semi-supervised user geolocation via graph convolutional networks,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers , 2018, pp. 2009–2019
2019
Later among the works it cites.
2019
Later among the works it cites.
W. Zheng, L. Li, Z. Zhang, Y. Huang, and L. Wang, “Relational network for skeleton-based action recognition,” in IEEE International Conference on Multimedia and Expo, ICME 2019, Shanghai, China, July 8-12, 2019 , 2019, pp. 826–831
2019
Later among the works it cites.
T. Wang, R. M. Anwer, M. H. Khan, F. S. Khan, Y. Pang, L. Shao, and J. Laaksonen, “Deep contextual attention for human-object interaction detection,” in 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019 . IEEE, 2019, pp. 5693–5701
2019
Later among the works it cites.
R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen, “Cross attention network for few-shot classification,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019, pp. 4005–4016
2019
Later among the works it cites.
J. Zhang, K. J. Shih, A. Elgammal, A. Tao, and B. Catanzaro, “Graphical contrastive losses for scene graph parsing,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 , 2019, pp. 11 535–11 543
2019
Later among the works it cites.
K. Tang, Y. Niu, J. Huang, J. Shi, and H. Zhang, “Unbiased scene graph generation from biased training,” 2020
2020
Closest in time.
2020
Closest in time.
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask R-CNN,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 2, pp. 386–397, 2020
2020
Closest in time.