Abstract
Video semantic segmentation (VSS) plays a crucial role in various realistic applications, such as unmanned vehicles, autonomous robots, and augmented reality. Despite the significant progress achieved in this field, balancing accuracy and efficiency remains a significant challenge. This paper presents a novel dual-branch hybrid network of CNN and Transformer with adaptive keyframe scheduling (DHN–AKS) to achieve higher accuracy and faster inference times for VSS. One branch \(Net^T\) uses a hierarchical transformer to extract high-level features on keyframes beneficial for segmentation accuracy in consideration of transformer’s powerful ability of modeling global semantic information. The other branch \( Net^C \) uses a lightweight feature network (ResNet-18) to extract the low-level features on non-keyframes beneficial for segmentation efficiency. Moreover, we present a dynamically updating memory matrix that memorizes the significant semantic information of historical video frames, enabling the exploration of the temporal relevance of the current frame based on cross attention. Experiments on two benchmark data sets, Cityscapes and CamVid, demonstrate that our proposed framework achieves competitive performance in terms of accuracy and inference time against some previous state-of-the-art methods.
Similar content being viewed by others
Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
References
Li, Y., Shi, J., Lin, D.: Low-latency video semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5997–6005 (2018)
Hu, P., Caba, F., Wang, O., Lin, Z., Sclaroff, S., Perazzi, F.: Temporally distributed networks for fast video semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8818–8827 (2020)
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272 (2021)
Wang, H., Wang, W., Liu, J.: Temporal memory attention for video semantic segmentation. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2254–2258 (2021). IEEE
Jin, Y., Han, D., Ko, H.: Trseg: transformer for semantic segmentation. Pattern Recognit. Lett. 148, 29–35 (2021)
Lazarević, M.: Stability and stabilization of fractional order time delay systems. Sci. Tech. Rev. 61(1), 31–45 (2011)
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. Adv. Neural Inf. Process. Syst. 34, 12077–12090 (2021)
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)
Wu, S., Wu, T., Lin, F., Tian, S., Guo, G.: Fully transformer networks for semantic image segmentation. arXiv preprint arXiv:2106.04108 (2021)
Duan, Z., Huang, X., Ma, J.: Transformer-based cross-modal information fusion network for semantic segmentation. Neural Process. Lett. 1–15 (2023)
Qin, Z., Liu, J., Zhang, X., Tian, M., Zhou, A., Yi, S., Li, H.: Pyramid fusion transformer for semantic segmentation. arXiv preprint arXiv:2201.04019 (2022)
Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: Deep feature flow for video recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2349–2358 (2017)
Paul, M., Danelljan, M., Van Gool, L., Timofte, R.: Local memory attention for fast video semantic segmentation. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1102–1109 (2021). IEEE
Li, J., Wang, W., Chen, J., Niu, L., Si, J., Qian, C., Zhang, L.: Video semantic segmentation via sparse temporal transformer. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 59–68 (2021)
Gadde, R., Jampani, V., Gehler, P.V.: Semantic video cnns through representation warping. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4453–4462 (2017)
Jin, X., Li, X., Xiao, H., Shen, X., Lin, Z., Yang, J., Chen, Y., Dong, J., Liu, L., Jie, Z., et al.: Video scene parsing with predictive feature learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5580–5588 (2017)
Kundu, A., Vineet, V., Koltun, V.: Feature space optimization for semantic video segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3168–3175 (2016)
Nilsson, D., Sminchisescu, C.: Semantic video segmentation by gated recurrent flow propagation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6819–6828 (2018)
Shelhamer, E., Rakelly, K., Hoffman, J., Darrell, T.: Clockwork convnets for video semantic segmentation. In: Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pp. 852–868 (2016). Springer
Liu, Y., Shen, C., Yu, C., Wang, J.: Efficient semantic video segmentation with per-frame inference. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16, pp. 352–368 (2020). Springer
Lin, L., Fan, H., Zhang, Z., Xu, Y., Ling, H.: Swintrack: a simple and strong baseline for transformer tracking. Adv. Neural Inf. Process. Syst. 35, 16743–16754 (2022)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: herarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2019)
Yu, C., Wang, J., Gao, C., Yu, G., Shen, C., Sang, N.: Context prior for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12416–12425 (2020)
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Zhou, Y., Zheng, X., Ouyang, W., Li, B.: A strip dilated convolutional network for semantic segmentation. Neural Process. Lett. 1–21 (2022)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241 (2015). Springer
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925–1934 (2017)
Mohan, R., Valada, A.: EfficientPS: efficient panoptic segmentation. Int. J. Comput. Vis. 129(5), 1551–1579 (2021)
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Sun, G., Liu, Y., Ding, H., Probst, T., Van Gool, L.: Coarse-to-fine feature mining for video semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3126–3137 (2022)
Jain, S., Wang, X., Gonzalez, J.E.: Accel: A corrective fusion network for efficient semantic segmentation on video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8866–8875 (2019)
Liu, J., Xu, X., Shi, Y., Deng, C., Shi, M.: RelaxNet: residual efficient learning and attention expected fusion network for real-time semantic segmentation. Neurocomputing 474, 115–127 (2022)
Brostow, G.J., Shotton, J., Fauqueur, J., Cipolla, R.: Segmentation and recognition using structure from motion point clouds. In: Computer Vision–ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I 10, pp. 44–57 (2008). Springer
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009). IEEE
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convNet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., Hu, H.: Video swin transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3202–3211 (2022)
Author information
Authors and Affiliations
Contributions
ZL wrote the main manuscript text, BZ performed the data analysis, and WD provided the methodology.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Communicated by J. Gao.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liang, Z., Dong, W. & Zhang, B. A dual-branch hybrid network of CNN and transformer with adaptive keyframe scheduling for video semantic segmentation. Multimedia Systems 30, 67 (2024). https://doi.org/10.1007/s00530-024-01262-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00530-024-01262-7