{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:44:03Z","timestamp":1760240643925,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,4]],"date-time":"2019-08-04T00:00:00Z","timestamp":1564876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702350","61802355"],"award-info":[{"award-number":["61702350","61802355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Semi-supervised video object segmentation (VOS) has obtained significant progress in recent years. The general purpose of VOS methods is to segment objects in video sequences provided with a single annotation in the first frame. However, many of the recent successful methods heavily fine-tune the object mask in the first frame, which decreases their efficiency. In this work, to address this issue, we propose a symmetry encoder-decoder network with the attention mechanism for video object segmentation (SAVOS) requiring only one forward pass to segment the target object in a video. Specifically, the encoder generates a low-resolution mask with smoothed boundaries, while the decoder further refines the details of the segmentation mask and integrates lower level features progressively. Besides, to obtain accurate segmentation results, we sequentially apply the attention module on multi-scale feature maps for refinement. We conduct several experiments on three challenging datasets (i.e., DAVIS 2016, DAVIS 2017, and SegTrack v2) to show that SAVOS achieves competitive performance against the state-of-the-art.<\/jats:p>","DOI":"10.3390\/sym11081006","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T03:25:22Z","timestamp":1564975522000},"page":"1006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Symmetry Encoder-Decoder Network with Attention Mechanism for Fast Video Object Segmentation"],"prefix":"10.3390","volume":"11","author":[{"given":"Mingyue","family":"Guo","sequence":"first","affiliation":[{"name":"College of Information and Engineering, Sichuan Agricultural University, Yaan 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-534X","authenticated-orcid":false,"given":"Dejun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Engineering, Sichuan Agricultural University, Yaan 625014, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information and Engineering, Sichuan Agricultural University, Yaan 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiqi","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ji, Z., Xiong, K., Pang, Y., and Li, X. 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