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Abstract
In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently. To achieve this, SeqFormer locates an instance in each frame and aggregates temporal information to learn a powerful representation of a video-level instance, which is used to predict the mask sequences on each frame dynamically. Instance tracking is achieved naturally without tracking branches or post-processing. On YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP with a ResNet-101 backbone without bells and whistles. Such achievement significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4, respectively. In addition, integrated with the recently-proposed Swin transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer could be a strong baseline that fosters future research in video instance segmentation, and in the meantime, advances this field with a more robust, accurate, neat model. The code is available athttps://github.com/wjf5203/SeqFormer.
J. Wu and W. Zhang—Work done during an internship at ByteDance.
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References
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: ViViT: a video vision transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6836–6846 (2021)
Athar, A., Mahadevan, S., Os̆ep, A., Leal-Taixé, L., Leibe, B.: STEm-Seg: spatio-temporal embeddings for instance segmentation in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 158–177. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58621-8_10
Bertasius, G., Torresani, L.: Classifying, segmenting, and tracking object instances in video with mask propagation. In: CVPR (2020)
Cao, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.: SipMask: spatial information preservation for fast image and video instance segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 1–18. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58568-6_1
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, X., Girshick, R., He, K., Dollár, P.: Tensormask: a foundation for dense object segmentation. In: ICCV (2019)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprintarXiv:2010.11929 (2020)
Fang, Y., et al.: Instances as queries. In: ICCV (2021)
Fu, Y., Yang, L., Liu, D., Huang, T.S., Shi, H.: Compfeat: comprehensive feature aggregation for video instance segmentation. arXiv preprintarXiv:2012.03400 (2020)
Goel, V., Li, J., Garg, S., Maheshwari, H., Shi, H.: MSN: efficient online mask selection network for video instance segmentation. arXiv preprintarXiv:2106.10452 (2021)
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Huang, Z., Huang, L., Gong, Y., Huang, C., Wang, X.: Mask scoring R-CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6409–6418 (2019)
Hwang, S., Heo, M., Oh, S.W., Kim, S.J.: Video instance segmentation using inter-frame communication transformers. In: NeurIPS (2021)
Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q.2(1–2), 83–97 (1955)
Li, K., et al.: Uniformer: unified transformer for efficient spatiotemporal representation learning. arXiv preprintarXiv:2201.04676 (2022)
Li, M., Li, S., Li, L., Zhang, L.: Spatial feature calibration and temporal fusion for effective one-stage video instance segmentation. In: CVPR (2021)
Lin, H., Wu, R., Liu, S., Lu, J., Jia, J.: Video instance segmentation with a propose-reduce paradigm. arXiv preprintarXiv:2103.13746 (2021)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).https://doi.org/10.1007/978-3-319-10602-1_48
Liu, D., Cui, Y., Tan, W., Chen, Y.: SG-Net: spatial granularity network for one-stage video instance segmentation. In: CVPR (2021)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Liu, X., et al.: End-to-end temporal action detection with transformer. IEEE Trans. Image Process. (TIP)31, 5427–5441 (2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. arXiv preprintarXiv:2103.14030 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)
Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: TrackFormer: multi-object tracking with transformers. arXiv preprintarXiv:2101.02702 (2021)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV) (2016)
Nguyen, T.C., Tang, T.N., Phan, N.L., Nguyen, C.H., Yamazaki, M., Yamanaka, M.: 1st place solution for youtubevos challenge 2021: video instance segmentation. arXiv preprintarXiv:2106.06649 (2021)
Patrick, M., et al.: Keeping your eye on the ball: trajectory attention in video transformers. arXiv preprintarXiv:2106.05392 (2021)
Qi, J., et al.: Occluded video instance segmentation: a benchmark. Int. J. Comput. Vis. 1–18 (2022)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NeurIPS (2015)
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: CVPR (2019)
Sun, P., et al.: Transtrack: multiple object tracking with transformer. arXiv preprintarXiv:2012.15460 (2020)
Sun, P., et al.: Sparse R-CNN: end-to-end object detection with learnable proposals. In: CVPR (2021)
Tian, Z., Shen, C., Chen, H.: Conditional convolutions for instance segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 282–298. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58452-8_17
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: ICCV (2019)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)
Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: SOLO: segmenting objects by locations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 649–665. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58523-5_38
Wang, Y., et al.: End-to-end video instance segmentation with transformers. In: CVPR (2021)
Wu, J., Jiang, Y., Sun, P., Yuan, Z., Luo, P.: Language as queries for referring video object segmentation. In: CVPR, pp. 4974–4984 (2022)
Xie, E., et al.: Polarmask: single shot instance segmentation with polar representation. In: CVPR, pp. 12193–12202 (2020)
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)
Xu, N., et al.: Youtubevis dataset 2021 version.https://youtube-vos.org/dataset/vis/
Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning spatio-temporal transformer for visual tracking. In: ICCV (2021)
Yang, L., Fan, Y., Xu, N.: Video instance segmentation. In: ICCV (2019)
Yang, S., et al.: Crossover learning for fast online video instance segmentation. arXiv preprintarXiv:2104.05970 (2021)
Zhao, Y., Xiong, Y., Lin, D.: Trajectory convolution for action recognition. In: NeurIPS (2018)
Zheng, S., 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)
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprintarXiv:2010.04159 (2020)
Acknowledgment
We thank Xiaoding Yuan for the support and discussions about implementation details. We thank the anonymous reviewers for their efforts and valuable feedback to improve our work.
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Huazhong University of Science and Technology, Wuhan, China
Junfeng Wu, Wenqing Zhang & Xiang Bai
ByteDance Inc., Singapore, Singapore
Yi Jiang & Song Bai
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Tel Aviv University, Tel Aviv, Israel
Shai Avidan
University College London, London, UK
Gabriel Brostow
Google AI, Accra, Ghana
Moustapha Cissé
University of Catania, Catania, Italy
Giovanni Maria Farinella
Facebook (United States), Menlo Park, CA, USA
Tal Hassner
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Wu, J., Jiang, Y., Bai, S., Zhang, W., Bai, X. (2022). SeqFormer: Sequential Transformer for Video Instance Segmentation. 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 13688. Springer, Cham. https://doi.org/10.1007/978-3-031-19815-1_32
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