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Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected using bounding boxes followed by prediction of their pairwise relationships.
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Proceedings of the European Conference on Computer Vision (ECCV) (2014)
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Johnson, J., Krishna, R., Stark, M., Li, L.J., Shamma, D., Bernstein, M., Fei-Fei, L.: Image retrieval using scene graphs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
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Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems (2015)
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Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., Manning, C.D.: Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In: Proceedings of the fourth workshop on vision and language (2015)
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
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Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 852–869 (2016)
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Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision (3DV) (2016)
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Dai, B., Zhang, Y., Lin, D.: Detecting visual relationships with deep relational networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
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Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., Bernstein, M., Fei-Fei, L.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision (IJCV) (2017)
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Peyre, J., Sivic, J., Laptev, I., Schmid, C.: Weakly-supervised learning of visual relations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2017)
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Qi, M., Wang, Y., Li, A.: Online cross-modal scene retrieval by binary representation and semantic graph. In: Proceedings of the ACM International Conference on Multimedia (ACM MM) (2017)
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Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
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Zhang, H., Kyaw, Z., Chang, S.F., Chua, T.S.: Visual translation embedding network for visual relation detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
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Zhang, J., Elhoseiny, M., Cohen, S., Chang, W., Elgammal, A.: Relationship proposal networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
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Aditya, S., Yang, Y., Baral, C., Aloimonos, Y., Fermüller, C.: Image understanding using vision and reasoning through scene description graph. Computer Vision and Image Understanding (2018)
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Chao, Y.W., Liu, Y., Liu, X., Zeng, H., Deng, J.: Learning to detect human-object interactions. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2018)
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Gao, L., Wang, B., Wang, W.: Image captioning with scene-graph based semantic concepts. In: Proceedings of the International Conference on Machine Learning and Computing (2018)
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Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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Kato, K., Li, Y., Gupta, A.: Compositional learning for human object interaction. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
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Li, Y., Ouyang, W., Zhou, B., Shi, J., Zhang, C., Wang, X.: Factorizable net: an efficient subgraph-based framework for scene graph generation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
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Yang, J., Lu, J., Lee, S., Batra, D., Parikh, D.: Graph r-cnn for scene graph generation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
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Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: Scene graph parsing with global context. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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2019
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Chen, T., Yu, W., Chen, R., Lin, L.: Knowledge-embedded routing network for scene graph generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Gu, J., Zhao, H., Lin, Z., Li, S., Cai, J., Ling, M.: Scene graph generation with external knowledge and image reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Hudson, D.A., Manning, C.D.: Gqa: A new dataset for real-world visual reasoning and compositional question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Kirillov, A., Girshick, R., He, K., Dollár, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Wang, T., Yang, T., Danelljan, M., Khan, F.S., Zhang, X., Sun, J.: Learning human-object interaction detection using interaction points. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
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Xu, P., Chang, X., Guo, L., Huang, P.Y., Chen, X., Hauptmann, A.G.: A survey of scene graph: Generation and application. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) (2020)
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Zareian, A., Karaman, S., Chang, S.F.: Bridging knowledge graphs to generate scene graphs. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
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Zareian, A., Wang, Z., You, H., Chang, S.: Learning visual commonsense for robust scene graph generation. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
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Kirillov, A., He, K., Girshick, R., Rother, C., Dollár, P.: Panoptic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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Kolesnikov, A., Kuznetsova, A., Lampert, C., Ferrari, V.: Detecting visual relationships using box attention. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (CVPR-W) (2019)
2019
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Li, Y.L., Zhou, S., Huang, X., Xu, L., Ma, Z., Fang, H.S., Wang, Y., Lu, C.: Transferable interactiveness knowledge for human-object interaction detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Liang, Y., Bai, Y., Zhang, W., Qian, X., Zhu, L., Mei, T.: Vrr-vg: Refocusing visually-relevant relationships. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
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Qi, M., Li, W., Yang, Z., Wang, Y., Luo, J.: Attentive relational networks for mapping images to scene graphs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Shi, J., Zhang, H., Li, J.: Explainable and explicit visual reasoning over scene graphs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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Tang, K., Zhang, H., Wu, B., Luo, W., Liu, W.: Learning to compose dynamic tree structures for visual contexts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
2019
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Wang, T., Anwer, R.M., Khan, M.H., Khan, F.S., Pang, Y., Shao, L., Laaksonen, J.: Deep contextual attention for human-object interaction detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
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Zhou, T., Wang, W., Qi, S., Ling, H., Shen, J.: Cascaded human-object interaction recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
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2021
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2021
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Desai, A., Wu, T.Y., Tripathi, S., Vasconcelos, N.: Learning of visual relations: The devil is in the tails. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
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Hou, Z., Yu, B., Qiao, Y., Peng, X., Tao, D.: Affordance transfer learning for human-object interaction detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Khandelwal, S., Suhail, M., Sigal, L.: Segmentation-grounded scene graph generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
2021
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Kim, B., Lee, J., Kang, J., Kim, E.S., Kim, H.J.: Hotr: End-to-end human-object interaction detection with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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2021
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Suhail, M., Mittal, A., Siddiquie, B., Broaddus, C., Eledath, J., Medioni, G., Sigal, L.: Energy-based learning for scene graph generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Tamura, M., Ohashi, H., Yoshinaga, T.: Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Wang, W.: Mmscenegraph (2021), https://github.com/Kenneth-Wong/MMSceneGraph
2021
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Ye, K., Kovashka, A.: Linguistic structures as weak supervision for visual scene graph generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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Zhang, A., Liao, Y., Liu, S., Lu, M., Wang, Y., Gao, C., Li, X.: Mining the benefits of two-stage and one-stage hoi detection. Proceedings of Advances in Neural Information Processing Systems (NeurIPS) (2021)
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Zhang, W., Pang, J., Chen, K., Loy, C.C.: K-Net: Towards unified image segmentation. In: Proceedings of Advances in Neural Information Processing Systems (NeurIPS) (2021)
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Zhong, Y., Shi, J., Yang, J., Xu, C., Li, Y.: Learning to generate scene graph from natural language supervision. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
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Zou, C., Wang, B., Hu, Y., Liu, J., Wu, Q., Zhao, Y., Li, B., Zhang, C., Zhang, C., Wei, Y., et al.: End-to-end human object interaction detection with hoi transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
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Amiri, S., Chandan, K., Zhang, S.: Reasoning with scene graphs for robot planning under partial observability. IEEE Robotics and Automation Letters (2022)
2022
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Gadre, S.Y., Ehsani, K., Song, S., Mottaghi, R.: Continuous scene representations for embodied ai. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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Li, L., Chen, L., Huang, Y., Zhang, Z., Zhang, S., Xiao, J.: The devil is in the labels: Noisy label correction for robust scene graph generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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Wang, S., Duan, Y., Ding, H., Tan, Y.P., Yap, K.H., Yuan, J.: Learning transferable human-object interaction detector with natural language supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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Yang, C.A., Tan, C.Y., Fan, W.C., Yang, C.F., Wu, M.L., Wang, Y.C.F.: Scene graph expansion for semantics-guided image outpainting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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Zhang, F.Z., Campbell, D., Gould, S.: Efficient two-stage detection of human-object interactions with a novel unary-pairwise transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Closest in time.