Fetching the paper…
Reading the bibliography…
Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene.
C. Goller and A. Kuchler, “Learning task-dependent distributed representations by backpropagation through structure,” in ICNN , 1996
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. D. Lafferty, A. McCallum, and F. C. N. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in ICML , 2001
2001
Earlier work this paper cites.
A. McCallum and W. Li, “Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons,” in HLT-NAACL , 2003
2003
Earlier work this paper cites.
A. Quattoni, M. Collins, and T. Darrell, “Conditional random fields for object recognition,” NIPS , 2004
2004
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in IJCNN , 2005
2005
Earlier work this paper cites.
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang, “A tutorial on energy-based learning,” Predicting structured data , vol. 1, no. 0, 2006
2006
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks , vol. 20, no. 1, pp. 61–80, 2008
2008
Earlier work this paper cites.
E. E. Aksoy, A. Abramov, F. Wörgötter, and B. Dellen, “Categorizing object-action relations from semantic scene graphs,” in ICRA , 2010
2010
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” in NIPS , 2011
2011
Earlier work this paper cites.
M. A. Sadeghi and A. Farhadi, “Recognition using visual phrases,” in CVPR , 2011
2011
Earlier work this paper cites.
B. Antoine, U. Nicolas, and G.-D. Alberto, “Translating embeddings for modeling multi-relational data,” in NIPS , 2013
2013
Earlier work this paper cites.
T. V. Nguyen, B. Ni, H. Liu, W. Xia, J. Luo, M. Kankanhalli, and S. Yan, “Image re-attentionizing,” IEEE Transactions on Multimedia , vol. 15, no. 8, pp. 1910–1919, 2013
2013
Earlier work this paper cites.
J. Bruna and S. Mallat, “Invariant scattering convolution networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 8, pp. 1872–1886, 2013
2013
Earlier work this paper cites.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in ICLR , 2013
2013
Earlier work this paper cites.
R. Speer and C. Havasi, “Conceptnet 5: A large semantic network for relational knowledge,” in The People’s Web Meets NLP , 2013, pp. 161–176
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu, in NIPS , 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV , 2014
2014
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR , 2014
2014
Earlier work this paper cites.
S. Schuster, R. Krishna, A. Chang, L. Fei-Fei, and C. D. Manning, “Generating semantically precise scene graphs from textual descriptions for improved image retrieval,” in VL@EMNLP , 2015
2015
Earlier work this paper cites.
J. Johnson, R. Krishna, M. Stark, L.-J. Li, D. Shamma, M. Bernstein, and L. Fei-Fei, “Image retrieval using scene graphs,” in CVPR , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr, “Conditional random fields as recurrent neural networks,” in ICCV , 2015
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR , 2015
2015
Earlier work this paper cites.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in AAAI , 2015
2015
Earlier work this paper cites.
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in ACL-IJCNLP , 2015, pp. 687–696
2015
Earlier work this paper cites.
S. Xie and Z. Tu, “Holistically-nested edge detection,” in ICCV , 2015
2015
Earlier work this paper cites.
V. Ramanathan, C. Li, J. Deng, W. Han, Z. Li, K. Gu, Y. Song, S. Bengio, C. Rosenberg, and L. Fei-Fei, “Learning semantic relationships for better action retrieval in images,” in CVPR , 2015
2015
Earlier work this paper cites.
J. Li, M. Luong, and D. Jurafsky, “A hierarchical neural autoencoder for paragraphs and documents,” in ACL , 2015
2015
Earlier work this paper cites.
R. Lin, S. Liu, M. Yang, M. Li, M. Zhou, and S. Li, “Hierarchical recurrent neural network for document modeling,” in EMNLP , 2015
2015
Earlier work this paper cites.
K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree-structured long short-term memory networks,” in ACL , 2015
2015
Earlier work this paper cites.
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” Computer Science , 2015
2015
Earlier work this paper cites.
Y. Chao, Z. Wang, Y. He, J. Wang, and J. Deng, “HICO: A benchmark for recognizing human-object interactions in images,” in ICCV , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Lu, R. Krishna, M. Bernstein, and L. Fei-Fei, “Visual relationship detection with language priors,” in ECCV , 2016
2016
Earlier work this paper cites.
X. Liang, X. Shen, J. Feng, L. Lin, and S. Yan, “Semantic object parsing with graph lstm,” in ECCV , 2016
2016
Earlier work this paper cites.
G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, “Neural architectures for named entity recognition,” in NAACL HLT , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in CVPR , 2016
2016
Earlier work this paper cites.
L. Wei, A. Dragomir, E. Dumitru, R. SzegedyScott, F. Cheng-Yang, and C. B. Alexander, “Ssd: Single shot multibox detector,” in ECCV , 2016
2016
Earlier work this paper cites.
K. Kang, W. Ouyang, H. Li, and X. Wang, “Object detection from video tubelets with convolutional neural networks,” in CVPR , 2016
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in CVPR , 2016
2016
Earlier work this paper cites.
Y. Xia, L. Zhang, Z. Liu, L. Nie, and X. Li, “Weakly supervised multimodal kernel for categorizing aerial photographs,” IEEE Transactions on Image Processing , vol. 26, no. 8, pp. 3748–3758, 2016
2016
Earlier work this paper cites.
H. Dai, B. Dai, and L. Song, “Discriminative embeddings of latent variable models for structured data,” in ICML , 2016
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in NIPS , 2016
2016
Earlier work this paper cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in ICML , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing, “Harnessing deep neural networks with logic rules,” in ACL , 2016
2016
Earlier work this paper cites.
X. Liang, L. Lee, and E. P. Xing, “Deep variation-structured reinforcement learning for visual relationship and attribute detection,” in CVPR , 2017
2017
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn:towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, no. 6, pp. 1137–1149, 2017
2017
Earlier work this paper cites.
R. Yu, A. Li, V. I. Morariu, and L. S. Davis, “Visual relationship detection with internal and external linguistic knowledge distillation,” in ICCV , 2017
2017
Earlier work this paper cites.
L. D. Dai Bo, Zhang Yuqi, “Detecting visual relationships with deep relational networks,” in CVPR , 2017
2017
Earlier work this paper cites.
A. Newell and J. Deng, “Pixels to graphs by associative embedding,” in NeurIPS , 2017
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017
2017
Earlier work this paper cites.
Y. Li, W. Ouyang, X. Wang, and X. Tang, “Vip-cnn: Visual phrase guided convolutional neural network,” in CVPR , 2017
2017
Earlier work this paper cites.
Y. Li, W. Ouyang, B. Zhou, K. Wang, and X. Wang, “Scene graph generation from objects, phrases and region captions,” in ICCV , 2017
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 CVPR , 2017
2017
Earlier work this paper cites.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma et al. , “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” IJCV , vol. 123, no. 1, pp. 32–73, 2017
2017
Earlier work this paper cites.
H. Zhang, Z. Kyaw, S.-F. Chang, and T.-S. Chua, “Visual translation embedding network for visual relation detection,” in CVPR , 2017
2017
Earlier work this paper cites.
B. Zhuang, L. Liu, C. Shen, and I. Reid, “Towards context-aware interaction recognition for visual relationship detection,” in ICCV , 2017
2017
Earlier work this paper cites.
J. Zhang, M. Elhoseiny, S. Cohen, W. Chang, and A. Elgammal, “Relationship proposal networks,” in CVPR , 2017
2017
Earlier work this paper cites.
K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang, “Object detection in videos with tubelet proposal networks,” in CVPR , 2017
2017
Earlier work this paper cites.
K. Masui, A. Ochiai, S. Yoshizawa, and H. Nakayama, “Recurrent visual relationship recognition with triplet unit,” in ISM , 2017
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Cited alongside, same era.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in ICML , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NIPS , 2017
2017
Cited alongside, same era.
J. Peyre, J. Sivic, I. Laptev, and C. Schmid, “Weakly-supervised learning of visual relations,” in ICCV , 2017
2017
Cited alongside, same era.
J. Zhang, Y. Kalantidis, M. Rohrbach, M. Paluri, A. Elgammal, and M. Elhoseiny, “Large-scale visual relationship understanding,” in AAAI , 2019
2019
Later among the works it cites.
B. Zhao, L. Meng, W. Yin, and L. Sigal, “Image generation from layout,” in CVPR , 2019
2019
Later among the works it cites.
S. Tripathi, S. Nittur Sridhar, S. Sundaresan, and H. Tang, “Compact scene graphs for layout composition and patch retrieval,” in CVPR Workshops , 2019
2019
Later among the works it cites.
B. Schroeder, S. Tripathi, and H. Tang, “Triplet-aware scene graph embeddings,” in ICCV Workshops , 2019
2019
Later among the works it cites.
L. Yikang, T. Ma, Y. Bai, N. Duan, S. Wei, and X. Wang, “Pastegan: A semi-parametric method to generate image from scene graph,” in NeurIPS , 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Shang, T. Ren, J. Guo, H. Zhang, and T.-S. Chua, “Video visual relation detection,” in ACM Multimedia , 2017
2017
Cited alongside, same era.
B. A. Plummer, A. Mallya, C. M. Cervantes, J. Hockenmaier, and S. Lazebnik, “Phrase localization and visual relationship detection with comprehensive image-language cues,” in ICCV , 2017
2017
Cited alongside, same era.
M. Y. Yang, W. Liao, H. Ackermann, and B. Rosenhahn, “On support relations and semantic scene graphs,” ISPRS journal of photogrammetry and remote sensing , vol. 131, pp. 15–25, 2017
2017
Cited alongside, same era.
R. Speer, J. Chin, and C. Havasi, “Conceptnet 5.5: An open multilingual graph of general knowledge,” in AAAI , 2017
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017
2017
Cited alongside, same era.
M. Qi, Y. Wang, and A. Li, “Online cross-modal scene retrieval by binary representation and semantic graph,” in ACM Multimedia , 2017
2017
Cited alongside, same era.
H. Qi, Y. Xu, T. Yuan, T. Wu, and S.-C. Zhu, “Scene-centric joint parsing of cross-view videos,” in AAAI , 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
X. Yang, K. Tang, H. Zhang, and J. Cai, “Auto-encoding scene graphs for image captioning,” in CVPR , 2019
2019
Later among the works it cites.
X. Li and S. Jiang, “Know more say less: Image captioning based on scene graphs,” IEEE Transactions on Multimedia , vol. 21, no. 8, pp. 2117–2130, 2019
2019
Later among the works it cites.
N. Xu, A.-A. Liu, J. Liu, W. Nie, and Y. Su, “Scene graph captioner: Image captioning based on structural visual representation,” Journal of Visual Communication and Image Representation , vol. 58, pp. 477–485, 2019
2019
Later among the works it cites.
J. Gu, S. Joty, J. Cai, H. Zhao, X. Yang, and G. Wang, “Unpaired image captioning via scene graph alignments,” in ICCV , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Liang, Y. Bai, W. Zhang, X. Qian, L. Zhu, and T. Mei, “Vrr-vg: Refocusing visually-relevant relationships,” in ICCV , 2019
2019
Later among the works it cites.
T. Zhuo, Z. Cheng, P. Zhang, Y. Wong, and M. Kankanhalli, “Explainable video action reasoning via prior knowledge and state transitions,” in ACM MM , 2019, pp. 521–529
2019
Later among the works it cites.
C. Zhang, W. Chao, and D. Xuan, “An empirical study on leveraging scene graphs for visual question answering,” in BMVC , 2019
2019
Later among the works it cites.
S. Kumar, S. Atreja, A. Singh, and M. Jain, “Adversarial adaptation of scene graph models for understanding civic issues,” in WWW , 2019
2019
Later among the works it cites.
M. Zhang, X. Liu, W. Liu, A. Zhou, H. Ma, and T. Mei, “Multi-granularity reasoning for social relation recognition from images,” in ICME , 2019
2019
Later among the works it cites.
B. Xu, Y. Wong, J. Li, Q. Zhao, and M. S. Kankanhalli, “Learning to detect human-object interactions with knowledge,” in CVPR , 2019
2019
Later among the works it cites.
W. Wang, R. Wang, S. Shan, and X. Chen, “Exploring context and visual pattern of relationship for scene graph generation,” in CVPR , 2019
2019
Later among the works it cites.
R. Wang, Z. Wei, P. Li, Q. Zhang, and X. Huang, “Storytelling from an image stream using scene graphs,” in AAAI , 2020
2020
Later among the works it cites.
T. Kaihua, N. Yulei, H. Jianqiang, S. Jiaxin, and Z. Hanwang, “Unbiased scene graph generation from biased training,” CVPR , pp. 3713–3122, 2020
2020
Later among the works it cites.
Z.-S. Hung, A. Mallya, and S. Lazebnik, “Contextual translation embedding for visual relationship detection and scene graph generation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, pp. 3820 – 3832, 2020
2020
Later among the works it cites.
G. Ren, L. Ren, Y. Liao, S. Liu, B. Li, J. Han, and S. Yan, “Scene graph generation with hierarchical context,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 2, pp. 909–915, 2020
2020
Later among the works it cites.
W. Wang, R. Wang, S. Shan, and X. Chen, “Sketching image gist: Human-mimetic hierarchical scene graph generation,” in ECCV , 2020
2020
Later among the works it cites.
M. Khademi and O. Schulte, “Deep generative probabilistic graph neural networks for scene graph generation,” in AAAI , 2020
2020
Later among the works it cites.
R. Wang, Z. Wei, P. Li, Q. Zhang, and X. Huang, “Storytelling from an image stream using scene graphs,” in AAAI , 2020
2020
Later among the works it cites.
S. Yan, C. Shen, Z. Jin, J. Huang, R. Jiang, Y. Chen, and X.-S. Hua, “Pcpl: Predicate-correlation perception learning for unbiased scene graph generation,” in ACM MM , 2020
2020
Later among the works it cites.
M. Raboh, R. Herzig, J. Berant, G. Chechik, and A. Globerson, “Differentiable scene graphs,” in WACV , 2020
2020
Later among the works it cites.
A. Zareian, S. Karaman, and S.-F. Chang, “Bridging knowledge graphs to generate scene graphs,” in ECCV , 2020
2020
Later among the works it cites.
A. Zareian, Z. Wang, H. You, and S. Chang, “Learning visual commonsense for robust scene graph generation,” in ECCV , 2020
2020
Later among the works it cites.
J. Ji, R. Krishna, L. Fei-Fei, and J. C. Niebles, “Action genome: Actions as compositions of spatio-temporal scene graphs,” in CVPR , 2020
2020
Later among the works it cites.
N. Gkanatsios, V. Pitsikalis, and P. Maragos, “From saturation to zero-shot visual relationship detection using local context,” in BMVC , 2020
2020
Later among the works it cites.
Y. Guo, J. Song, L. Gao, and H. T. Shen, “One-shot scene graph generation,” in ACM Multimedia , 2020
2020
Later among the works it cites.
B. Knyazev, H. de Vries, C. Cangea, G. W. Taylor, A. C. Courville, and E. Belilovsky, “Graph density-aware losses for novel compositions in scene graph generation,” in BMVC , 2020
2020
Later among the works it cites.
T. He, L. Gao, J. Song, J. Cai, and Y. Li, “Learning from the scene and borrowing from the rich: Tackling the long tail in scene graph generation,” in IJCAI , 2020
2020
Later among the works it cites.
U.-H. Kim, J.-M. Park, T.-J. Song, and J.-H. Kim, “3-d scene graph: A sparse and semantic representation of physical environments for intelligent agents,” IEEE transactions on cybernetics , pp. 4921–4933, 2020
2020
Later among the works it cites.
R. Herzig, A. Bar, H. Xu, G. Chechik, T. Darrell, and A. Globerson, “Learning canonical representations for scene graph to image generation,” in ECCV , 2020
2020
Later among the works it cites.
H. Dhamo, A. Farshad, I. Laina, N. Navab, G. D. Hager, F. Tombari, and C. Rupprecht, “Semantic image manipulation using scene graphs,” in CVPR , 2020
2020
Later among the works it cites.
S. Chen, Q. Jin, P. Wang, and Q. Wu, “Say as you wish: Fine-grained control of image caption generation with abstract scene graphs,” in CVPR , 2020
2020
Later among the works it cites.
Y. Zhong, L. Wang, J. Chen, D. Yu, and Y. Li, “Comprehensive image captioning via scene graph decomposition,” in ECCV , 2020
2020
Later among the works it cites.
B. Schroeder and S. Tripathi, “Structured query-based image retrieval using scene graphs,” in CVPR Workshops , 2020
2020
Later among the works it cites.
S. Wang, R. Wang, Z. Yao, S. Shan, and X. Chen, “Cross-modal scene graph matching for relationship-aware image-text retrieval,” in WACV , 2020
2020
Later among the works it cites.
L. Chen, G. Lin, S. Wang, and Q. Wu, “Graph edit distance reward: Learning to edit scene graph,” in ECCV , 2020
2020
Later among the works it cites.
J. Wald, H. Dhamo, N. Navab, and F. Tombari, “Learning 3d semantic scene graphs from 3d indoor reconstructions,” in CVPR , 2020
2020
Later among the works it cites.
J. Li, Y. Wong, Q. Zhao, and M. S. Kankanhalli, “Visual social relationship recognition,” Int. J. Comput. Vis. , vol. 128, no. 6, pp. 1750–1764, 2020
2020
Later among the works it cites.
X. Lin, C. Ding, J. Zeng, and D. Tao, “Gps-net: Graph property sensing network for scene graph generation,” in CVPR , 2020
2020
Later among the works it cites.
S. Inuganti and V. Balasubramanian, “Assisting scene graph generation with self-supervision,” arXiv , 2020
2020
Later among the works it cites.
T. J. Wang, S. Pehlivan, and J. Laaksonen, “Tackling the unannotated: Scene graph generation with bias-reduced models,” in BMVC , 2020
2020
Later among the works it cites.
M. Wei, C. Yuan, X. Yue, and K. Zhong, “Hose-net: Higher order structure embedded network for scene graph generation,” ACM MM , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
P. Ren, Y. Xiao, X. Chang, P. Huang, Z. Li, X. Chen, and X. Wang, “A comprehensive survey of neural architecture search: Challenges and solutions,” ACM Comput. Surv. , vol. 54, no. 4, pp. 76:1–76:34, 2021
2021
Closest in time.
C. Yan, X. Chang, Z. Li, W. Guan, Z. Ge, L. Zhu, and Q. Zheng, “Zeronas: Differentiable generative adversarial networks search for zero-shot learning,” IEEE Trans. Pattern Anal. Mach. Intell. , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
M. Suhail, A. Mittal, B. Siddiquie, C. Broaddus, J. Eledath, G. Medioni, and L. Sigal, “Energy-based learning for scene graph generation,” in CVPR , 2021
2021
Closest in time.
H. Liu, N. Yan, M. Mortazavi, and B. Bhanu, “Fully convolutional scene graph generation,” in CVPR , 2021
2021
Closest in time.
J. Jiang, Z. He, S. Zhang, X. Zhao, and J. Tan, “Learning to transfer focus of graph neural network for scene graph parsing,” Pattern Recognition , vol. 112, p. 107707, 2021
2021
Closest in time.
A.-A. Liu, H. Tian, N. Xu, W. Nie, Y. Zhang, and M. Kankanhalli, “Toward region-aware attention learning for scene graph generation,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Closest in time.
N. Dhingra, F. Ritter, and A. Kunz, “Bgt-net: Bidirectional gru transformer network for scene graph generation,” in CVPR , 2021
2021
Closest in time.
K. Ye and A. Kovashka, “Linguistic structures as weak supervision for visual scene graph generation,” in CVPR , 2021
2021
Closest in time.
G. Yang, J. Zhang, Y. Zhang, B. Wu, and Y. Yang, “Probabilistic modeling of semantic ambiguity for scene graph generation,” in CVPR , 2021
2021
Closest in time.
S. Sharifzadeh, S. M. Baharlou, and V. Tresp, “Classification by attention: Scene graph classification with prior knowledge,” in AAAI , 2021
2021
Closest in time.
I. Misra, C. Lawrence Zitnick, M. Mitchell, and R. Girshick, “Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels,” in CVPR , 2016
2021
Closest in time.
P. Ren, Y. Xiao, X. Chang, P. Huang, Z. Li, B. B. Gupta, X. Chen, and X. Wang, “A survey of deep active learning,” ACM Comput. Surv. , vol. 54, no. 9, pp. 180:1–180:40, 2022
2022
Closest in time.