{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:06:05Z","timestamp":1743030365985,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031063800"},{"type":"electronic","value":"9783031063817"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06381-7_14","type":"book-chapter","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T08:03:16Z","timestamp":1652688196000},"page":"197-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Video Object Segmentation Based on\u00a0Guided Feature Transfer Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2289-2284","authenticated-orcid":false,"given":"Mustansar","family":"Fiaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arif","family":"Mahmood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sehar","family":"Shahzad Farooq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamran","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Shaheryar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0239-6785","authenticated-orcid":false,"given":"Soon Ki","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Bao, L., Wu, B., Liu, W.: CNN in MRF: video object segmentation via inference in a CNN-based higher-order spatio-temporal MRF. In: CVPR, pp. 5977\u20135986 (2018)","DOI":"10.1109\/CVPR.2018.00626"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Caelles, S., et al.: One-shot video object segmentation. In: CVPR, pp. 221\u2013230 (2017)","DOI":"10.1109\/CVPR.2017.565"},{"key":"14_CR3","unstructured":"Caelles, S., et al.: Fast video object segmentation with spatio-temporal GANs. arXiv preprint arXiv:1903.12161 (2019)"},{"issue":"4","key":"14_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"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\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Blazingly fast video object segmentation with pixel-wise metric learning. In: CVPR, pp. 1189\u20131198 (2018)","DOI":"10.1109\/CVPR.2018.00130"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, J., et al.: SegFlow: Joint learning for video object segmentation and optical flow. In: ICCV, pp. 686\u2013695 (2017)","DOI":"10.1109\/ICCV.2017.81"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Cheng, J., et al.: Fast and accurate online video object segmentation via tracking parts. In: CVPR, pp. 7415\u20137424 (2018)","DOI":"10.1109\/CVPR.2018.00774"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Ci, H., Wang, C., Wang, Y.: Video object segmentation by learning location-sensitive embeddings. In: ECCV, pp. 501\u2013516 (2018)","DOI":"10.1007\/978-3-030-01252-6_31"},{"key":"14_CR10","unstructured":"De Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., Courville, A.C.: Modulating early visual processing by language. In: Advances in Neural Information Processing Systems, pp. 6594\u20136604 (2017)"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"13","key":"14_CR12","doi-asserted-by":"publisher","first-page":"3780","DOI":"10.3390\/s20133780","volume":"20","author":"M Fiaz","year":"2020","unstructured":"Fiaz, M., Mahmood, A., Baek, K.Y., Farooq, S.S., Jung, S.K.: Improving object tracking by added noise and channel attention. Sensors 20(13), 3780 (2020)","journal-title":"Sensors"},{"issue":"2","key":"14_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3309665","volume":"52","author":"M Fiaz","year":"2019","unstructured":"Fiaz, M., Mahmood, A., Javed, S., Jung, S.K.: Handcrafted and deep trackers: Recent visual object tracking approaches and trends. ACM Comput. Surv. (CSUR) 52(2), 1\u201344 (2019)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"14","key":"14_CR14","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.3390\/s20144021","volume":"20","author":"M Fiaz","year":"2020","unstructured":"Fiaz, M., Mahmood, A., Jung, S.K.: Learning soft mask based feature fusion with channel and spatial attention for robust visual object tracking. Sensors 20(14), 4021 (2020)","journal-title":"Sensors"},{"key":"14_CR15","unstructured":"Fiaz, M., Mahmood, A., Jung, S.K.: Video object segmentation using guided feature and directional deep appearance learning. In: Proceedings of the 2020 DAVIS Challenge on Video Object Segmentation-CVPR, Workshops, Seattle, WA, USA, vol. 19 (2020)"},{"key":"14_CR16","doi-asserted-by":"publisher","unstructured":"Fiaz, M., et al.: Adaptive feature selection Siamese networks for visual tracking. In: Ohyama, W., Jung, S.K. (eds.) IW-FCV 2020. CCIS, vol. 1212, pp. 167\u2013179. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-4818-5_13","DOI":"10.1007\/978-981-15-4818-5_13"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Fiaz, M., Zaheer, M.Z., Mahmood, A., Lee, S.I., Jung, S.K.: 4G-VOS: video object segmentation using guided context embedding. Knowl. Based Syst. 231, 107401 (2021)","DOI":"10.1016\/j.knosys.2021.107401"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Hu, Y.T., Huang, J.B., Schwing, A.G.: Videomatch: Matching based video object segmentation. In: ECCV, pp. 54\u201370 (2018)","DOI":"10.1007\/978-3-030-01237-3_4"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV, pp. 1501\u20131510 (2017)","DOI":"10.1109\/ICCV.2017.167"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Jampani, V., Gadde, R., Gehler, P.V.: Video propagation networks. In: CVPR, pp. 451\u2013461 (2017)","DOI":"10.1109\/CVPR.2017.336"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Jang, W.D., Kim, C.S.: Online video object segmentation via convolutional trident network. In: CVPR, pp. 5849\u20135858 (2017)","DOI":"10.1109\/CVPR.2017.790"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Johnander, J., Danelljan, M., Brissman, E., Khan, F.S., Felsberg, M.: A generative appearance model for end-to-end video object segmentation. In: CVPR, pp. 8953\u20138962 (2019)","DOI":"10.1109\/CVPR.2019.00916"},{"key":"14_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Li, X., C. Loy, C.: Video object segmentation with joint re-identification and attention-aware mask propagation. In: ECCV, pp. 90\u2013105 (2018)","DOI":"10.1007\/978-3-030-01219-9_6"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Lin, H., Qi, X., Jia, J.: AGSS-VOS: attention guided single-shot video object segmentation. In: ICCV, pp. 3949\u20133957 (2019)","DOI":"10.1109\/ICCV.2019.00405"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Luke\u017ei\u010d, A., Matas, J., Kristan, M.: D3s-a discriminative single shot segmentation tracker. arXiv preprint arXiv:1911.08862 (2019)","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"14_CR28","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, vol. 30, p. 3 (2013)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., et al.: Video object segmentation without temporal information. IEEE Trans. Pattern Anal. Mach. Intell. 41(6), 1515\u20131530 (2018)","DOI":"10.1109\/TPAMI.2018.2838670"},{"key":"14_CR30","unstructured":"Nam, H., Kim, H.: Batch-instance normalization for adaptively style-invariant neural networks. In: Advances in Neural Information Processing System (2018)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Khoreva, A., Benenson, R., Schiele, B., Sorkine-Hornung, A.: Learning video object segmentation from static images. In: CVPR, pp. 2663\u20132672 (2017)","DOI":"10.1109\/CVPR.2017.372"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR, pp. 724\u2013732 (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"14_CR33","unstructured":"Pont-Tuset, J., Perazzi, F., Caelles, S., Arbel\u00e1ez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 davis challenge on video object segmentation. arXiv preprint arXiv:1704.00675 (2017)"},{"key":"14_CR34","doi-asserted-by":"publisher","first-page":"100857","DOI":"10.1109\/ACCESS.2020.2997917","volume":"8","author":"MM Rahman","year":"2020","unstructured":"Rahman, M.M., Fiaz, M., Jung, S.K.: Efficient visual tracking with stacked channel-spatial attention learning. IEEE Access 8, 100857\u2013100869 (2020)","journal-title":"IEEE Access"},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Tian, Z., He, T., Shen, C., Yan, Y.: Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation. In: CVPR, pp. 3126\u20133135 (2019)","DOI":"10.1109\/CVPR.2019.00324"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Yang, M.H., Black, M.J.: Video segmentation via object flow. In: CVPR, pp. 3899\u20133908 (2016)","DOI":"10.1109\/CVPR.2016.423"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Ventura, C., Bellver, M., Girbau, A., Salvador, A., Marques, F., Giro-i Nieto, X.: RVOS: end-to-end recurrent network for video object segmentation. In: CVPR, pp. 5277\u20135286 (2019)","DOI":"10.1109\/CVPR.2019.00542"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.C.: Feelvos: fast end-to-end embedding learning for video object segmentation. In: CVPR, pp. 9481\u20139490 (2019)","DOI":"10.1109\/CVPR.2019.00971"},{"key":"14_CR39","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Leibe, B.: Online adaptation of convolutional neural networks for the 2017 DAVIS challenge on video object segmentation. In: The 2017 DAVIS Challenge on VOS-CVPR Workshops, vol. 5 (2017)","DOI":"10.5244\/C.31.116"},{"key":"14_CR40","unstructured":"Voigtlaender, P., Luiten, J., Leibe, B.: BoLTVOS: box-level tracking for video object segmentation. arXiv preprint arXiv:1904.04552 (2019)"},{"key":"14_CR41","doi-asserted-by":"crossref","unstructured":"Wang, Q., et al.: Fast online object tracking and segmentation: a unifying approach. In: CVPR, pp. 1328\u20131338 (2019)","DOI":"10.1109\/CVPR.2019.00142"},{"key":"14_CR42","doi-asserted-by":"crossref","unstructured":"Wang, W., Shen, J., Porikli, F., Yang, R.: Semi-supervised video object segmentation with super-trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 985\u2013998 (2018)","DOI":"10.1109\/TPAMI.2018.2819173"},{"key":"14_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Z., Xu, J., Liu, L., Zhu, F., Shao, L.: RANet: ranking attention network for fast video object segmentation. In: ICCV, pp. 3978\u20133987 (2019)","DOI":"10.1109\/ICCV.2019.00408"},{"key":"14_CR44","doi-asserted-by":"crossref","unstructured":"Oh, S.W., et al.: Fast video object segmentation by reference-guided mask propagation. In: CVPR, pp. 7376\u20137385 (2018)","DOI":"10.1109\/CVPR.2018.00770"},{"key":"14_CR45","unstructured":"Xu, N., et al.: YouTube-VOS: a large-scale video object segmentation benchmark. arXiv preprint arXiv:1809.03327 (2018)"},{"key":"14_CR46","doi-asserted-by":"crossref","unstructured":"Yang, L., et al.: Efficient video object segmentation via network modulation. In: CVPR, pp. 6499\u20136507 (2018)","DOI":"10.1109\/CVPR.2018.00680"},{"key":"14_CR47","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: Anchor diffusion for unsupervised video object segmentation. In: ICCV, pp. 931\u2013940 (2019)","DOI":"10.1109\/ICCV.2019.00102"},{"key":"14_CR48","unstructured":"Zhou, Q., et al.: Proposal, tracking and segmentation (PTS): a cascaded network for video object segmentation. arXiv preprint arXiv:1907.01203 (2019)"},{"key":"14_CR49","unstructured":"Zhuo, T., Cheng, Z., Kankanhalli, M.: Fast video object segmentation via mask transfer network. arXiv preprint arXiv:1908.10717 (2019)"}],"container-title":["Communications in Computer and Information Science","Frontiers of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06381-7_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T08:17:06Z","timestamp":1652689026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06381-7_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031063800","9783031063817"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06381-7_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IW-FCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Frontiers of Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 February 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwfcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/iwfcv2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"63","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}