Abstract
Video object segmentation has becoming a hot research topic in the computer vision society, with a wide range of applications, such as autonomous driving, video editing, and video surveillance. However, due to the complexity of video data, video object segmentation still faces challenges like occlusion, object appearance changes, and similar objects. Previous methods mainly tackle this task by using the memory module, but the computation cost will linearly increase along with the length of the video. To deal with the issue of the previous memory-based method, we proposed a cascaded semi-supervised video object framework with an adaptive memory module. In addition, we use a cascaded instance tracker to find the object and reduce the image resolutions, and we further use a boundary estimation branch to improve the accuracy. Experimental results on several benchmarks demonstrate the effectiveness and efficiency of our proposed method.
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Acknowledgement
This work is supported by the National Nature Science Foundation of China (No. 61876159, 61806172, 62076116, U1705286).
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Yang, S., Luo, Z., Cao, D., Lin, D., Su, S., Li, S. (2022). A Semi-supervised Video Object Segmentation Method Based on Adaptive Memory Module. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_34
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DOI: https://doi.org/10.1007/978-981-19-4546-5_34
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