Abstract
Video relation grounding has attracted growing attention in the fields of video understanding and multimodal learning. While the past years have witnessed remarkable progress in this issue, the difficulties of multi-instance and complex temporal reasoning make it still a challenging task. In this paper, we propose a novel Asymmetric Relation Consistency (ARC) reasoning model to solve the video relation grounding problem. To overcome the multi-instance confusion problem, an asymmetric relation reasoning method and a novel relation consistency loss are proposed to ensure the consistency of the relationships across multiple instances. In order to precisely localize the relation instance in temporal context, a transformer-based relation reasoning module is proposed. Our model is trained in a weakly-supervised manner. The proposed method was tested on the challenging video relation dataset. Experiments manifest that the performance of our method outperforms the state-of-the-art methods by a large margin. Extensive ablation studies also prove the effectiveness and strength of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Chen, Z., Ma, L., Luo, W., Wong, K.Y.K.: Weakly-supervised spatio-temporally grounding natural sentence in video. In: The Annual Meeting of the Association for Computational Linguistics (2019)
Da, C., Zhang, Y., Zheng, Y., Pan, P., Xu, Y., Pan, C.: Asynce: disentangling false-positives for weakly-supervised video grounding. In: ACM International Conference on Multimedia (2021)
Ding, X., et al.: Support-set based cross-supervision for video grounding. In: IEEE CVPR (2021)
Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021)
Gao, C., Xu, J., Zou, Y., Huang, J.-B.: DRG: dual relation graph for human-object interaction detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 696–712. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_41
Gao, K., Chen, L., Huang, Y., Xiao, J.: Video relation detection via tracklet based visual transformer. In: ACM International Conference on Multimedia (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
Kim, B., Lee, J., Kang, J., Kim, E.S., Kim, H.J.: Hotr: end-to-end human-object interaction detection with transformers. In: IEEE CVPR (2021)
Krishna, R., Chami, I., Bernstein, M., Fei-Fei, L.: Referring relationships. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)
Li, J., Wei, P., Zhang, Y., Zheng, N.: A slow-i-fast-p architecture for compressed video action recognition. In: ACM International Conference on Multimedia (2020)
Li, Q., Tao, Q., Joty, S., Cai, J., Luo, J.: VQA-E: explaining, elaborating, and enhancing your answers for visual questions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 570–586. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_34
Li, Y., Yang, X., Shang, X., Chua, T.S.: Interventional video relation detection. In: ACM International Conference on Multimedia (2021)
Liao, Y., Liu, S., Wang, F., Chen, Y., Qian, C., Feng, J.: Ppdm: parallel point detection and matching for real-time human-object interaction detection. In: IEEE CVPR (2020)
Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 852–869. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_51
Ma, Z., Wei, P., Li, H., Zheng, N.: Hoig: end-to-end human-object interactions grounding with transformers. In: IEEE International Conference on Multimedia and Expo (2022)
Mi, L., Chen, Z.: Hierarchical graph attention network for visual relationship detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing (2014)
Qian, X., Zhuang, Y., Li, Y., Xiao, S., Pu, S., Xiao, J.: Video relation detection with spatio-temporal graph. In: ACM International Conference on Multimedia (2019)
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)
Shang, X., Li, Y., Xiao, J., Ji, W., Chua, T.S.: Video visual relation detection via iterative inference. In: ACM International Conference on Multimedia (2021)
Shang, X., Ren, T., Guo, J., Zhang, H., Chua, T.S.: Video visual relation detection. In: ACM International Conference on Multimedia (2017)
Shi, J., Xu, J., Gong, B., Xu, C.: Not all frames are equal: Weakly-supervised video grounding with contextual similarity and visual clustering losses. In: IEEE CVPR (2019)
Soldan, M., Xu, M., Qu, S., Tegner, J., Ghanem, B.: Vlg-net: video-language graph matching network for video grounding. In: IEEE/CVF International Conference on Computer Vision (2021)
Sun, X., Ren, T., Zi, Y., Wu, G.: Video visual relation detection via multi-modal feature fusion. In: ACM International Conference on Multimedia (2019)
Tamura, M., Ohashi, H., Yoshinaga, T.: Qpic: query-based pairwise human-object interaction detection with image-wide contextual information. In: IEEE CVPR (2021)
Tsai, Y.H.H., Divvala, S., Morency, L.P., Salakhutdinov, R., Farhadi, A.: Video relationship reasoning using gated spatio-temporal energy graph. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Vaswani, A., et al.: Attention is all you need. In: NIPS (2017)
Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence - video to text. In: IEEE International Conference on Computer Vision (2015)
Wang, T., Yang, T., Danelljan, M., Khan, F.S., Zhang, X., Sun, J.: Learning human-object interaction detection using interaction points. In: IEEE CVPR (2020)
Wang, W., Gao, J., Xu, C.: Weakly-supervised video object grounding via stable context learning. In: ACM International Conference on Multimedia (2021)
Wang, Y., Zhou, W., Li, H.: Fine-grained semantic alignment network for weakly supervised temporal language grounding. In: Findings of the Association for Computational Linguistics (2021)
Wei, P., Zhao, Y., Zheng, N., Zhu, S.C.: Modeling 4d human-object interactions for joint event segmentation, recognition, and object localization. In: IEEE Trans. Pattern Anal. Mach. Intell., 1165–1179 (2017)
Xiao, J., Shang, X., Yang, X., Tang, S., Chua, T.-S.: Visual relation grounding in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 447–464. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58539-6_27
Yu, R., Li, A., Morariu, V.I., Davis, L.S.: Visual relationship detection with internal and external linguistic knowledge distillation. In: IEEE International Conference on Computer Vision (2017)
Zhan, Y., Yu, J., Yu, T., Tao, D.: On exploring undetermined relationships for visual relationship detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Zhao, Y., Zhao, Z., Zhang, Z., Lin, Z.: Cascaded prediction network via segment tree for temporal video grounding. In: IEEE CVPR (2021)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision (2017)
Acknowledgement
This research was supported by the grants Key Research and Development Program of China (No. 2018AAA0102501), and National Natural Science Foundation of China (No. 61876149, No. 62088102).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, H., Wei, P., Li, J., Ma, Z., Shang, J., Zheng, N. (2022). Asymmetric Relation Consistency Reasoning for Video Relation Grounding. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13695. Springer, Cham. https://doi.org/10.1007/978-3-031-19833-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-19833-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19832-8
Online ISBN: 978-3-031-19833-5
eBook Packages: Computer ScienceComputer Science (R0)