{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T22:07:24Z","timestamp":1777068444152,"version":"3.51.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00371-022-02503-4","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T21:06:20Z","timestamp":1652303180000},"page":"2969-2980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["MFANet: Multi-scale feature fusion network with attention mechanism"],"prefix":"10.1007","volume":"39","author":[{"given":"Gaihua","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0218-925X","authenticated-orcid":false,"given":"Xin","family":"Gan","sequence":"additional","affiliation":[]},{"given":"Qingcheng","family":"Cao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1783-4573","authenticated-orcid":false,"given":"Qianyu","family":"Zhai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"issue":"9","key":"2503_CR1","doi-asserted-by":"publisher","first-page":"2806-14","DOI":"10.1093\/cercor\/bhu077","volume":"25","author":"M Sugiura","year":"2015","unstructured":"Sugiura, M., Miyauchi, C. M., Kotozaki, Y.: Neural mechanism for mirrored self-face recognition. Cereb. Cortex 25(9), 2806\u201314 (2015)","journal-title":"Cereb. Cortex"},{"issue":"5","key":"2503_CR2","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1016\/j.patcog.2005.10.013","volume":"39","author":"NV Boulgourisa","year":"2006","unstructured":"Boulgourisa, N.V., Plataniotis, K., Hatzinakos, D.: Gait recognition using linear time normalization. Pattern Recogn. 39(5), 969\u2013979 (2006)","journal-title":"Pattern Recogn."},{"issue":"15","key":"2503_CR3","doi-asserted-by":"publisher","first-page":"16915","DOI":"10.1109\/JSEN.2021.3078455","volume":"21","author":"J Mei","year":"2021","unstructured":"Mei, J., Zhou, D., Cao, J., et al.: HDINet: hierarchical dual-sensor interaction Network for RGBT tracking. IEEE Sens. J. 21(15), 16915\u201316926 (2021). https:\/\/doi.org\/10.1109\/JSEN.2021.3078455","journal-title":"IEEE Sens. J."},{"issue":"3","key":"2503_CR4","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1504\/IJCVR.2019.099435","volume":"9","author":"H Chaudhry","year":"2009","unstructured":"Chaudhry, H., Rahim, M. S. M., Saba, T.: Crowd detection and counting using a static and dynamic platform: state of the art. Int. J. Comput. Vis. Robot. 9(3), 228\u201359 (2009)","journal-title":"Int. J. Comput. Vis. Robot."},{"issue":"5","key":"2503_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00371-005-0287-1","volume":"21","author":"E Cerezo","year":"2005","unstructured":"Cerezo, E., P\u00e9rez, F., Pueyo, X.: A survey on participating media rendering techniques. Vis. Comput. 21(5), 303\u2013328 (2005)","journal-title":"Vis. Comput."},{"issue":"1","key":"2503_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"G Wang","year":"2021","unstructured":"Wang, G., Zhai, Q.: Feature fusion network based on strip pooling. Sci. Rep. 11(1), 1\u20138 (2021)","journal-title":"Sci. Rep."},{"key":"2503_CR7","first-page":"29","volume":"2","author":"R Verschae","year":"2005","unstructured":"Verschae, R., Ruiz-del-Solar, J.: Object detection: current and future directions. Front. Robot. AI 2, 29 (2005)","journal-title":"Front. Robot. AI"},{"issue":"33\/34","key":"2503_CR8","first-page":"23729-91","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao, Y., Tian, Z., Yu, J.: A review of object detection based on deep learning. Multimed. Tools Appl. 79(33\/34), 23729\u201391 (2020)","journal-title":"Multimed. Tools Appl."},{"key":"2503_CR9","doi-asserted-by":"crossref","unstructured":"Gkioxari, G., Girshick, R., Malik, J.: Contextual action recognition with r* cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1080\u20131088 (2015)","DOI":"10.1109\/ICCV.2015.129"},{"issue":"6","key":"2503_CR10","doi-asserted-by":"publisher","first-page":"1137-49","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u201349 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2503_CR11","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"2503_CR12","doi-asserted-by":"crossref","unstructured":"Cao, J., Cholakkal, H., Anwer, R.M., Khan, F.S., Pang, Y., Shao, L.: D2det: towards high quality object detection and instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11485\u201311494 (2020)","DOI":"10.1109\/CVPR42600.2020.01150"},{"key":"2503_CR13","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Luo, P.: Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454\u201314463 (2021)","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"2503_CR14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2503_CR15","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2503_CR16","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"2503_CR17","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: Reppoints: Point set representation for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9657\u20139666 (2019)","DOI":"10.1109\/ICCV.2019.00975"},{"issue":"2","key":"2503_CR18","doi-asserted-by":"publisher","first-page":"318-27","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u201327 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2503_CR19","doi-asserted-by":"crossref","unstructured":"Kim, K., Lee, H.S.: Probabilistic anchor assignment with IOU prediction for object detection. In: European Conference on Computer Vision, pp. 355\u2013371. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-58595-2_22"},{"key":"2503_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N.: Varifocalnet: An IOU-aware dense object detector. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8514\u20138523 (2021)","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"2503_CR21","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., Sun, J.: You only look one-level feature. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13039\u201313048 (2021)","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"2503_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9759\u20139768 (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"2503_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125(2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2503_CR24","doi-asserted-by":"crossref","unstructured":"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\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"2503_CR25","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., Le, Q.V.: Nas-fpn: Learning scalable feature pyramid architecture for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7036\u20137045(2019)","DOI":"10.1109\/CVPR.2019.00720"},{"key":"2503_CR26","doi-asserted-by":"crossref","unstructured":"Guo, C., Fan, B., Zhang, Q., Xiang, S., Pan, C.: Augfpn: Improving multi-scale feature learning for object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12595\u201312604 (2020)","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"2503_CR27","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"2503_CR28","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., Yuille, A.: Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp. 10213\u201310224 (2021)","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"2503_CR29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2503_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2503_CR31","doi-asserted-by":"crossref","unstructured":"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\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"2503_CR32","doi-asserted-by":"crossref","unstructured":"Jiang, X., Zhang, L., Xu, M., Zhang, T., Lv, P., Zhou, B., Pang, Y.: Attention scaling for crowd counting. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4706\u20134715 (2020)","DOI":"10.1109\/CVPR42600.2020.00476"},{"key":"2503_CR33","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u201313722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"2503_CR34","doi-asserted-by":"publisher","first-page":"7389-98","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong, T., Sun, F., Liu, H.: FoveaBox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389\u201398 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"2503_CR35","doi-asserted-by":"crossref","unstructured":"Li, D., Huang, C., Liu, Y.: YOLOv3 target detection algorithm based on channel attention mechanism. In: 2021 3rd International Conference on Natural Language Processing (ICNLP), pp. 179\u2013183. IEEE (2021)","DOI":"10.1109\/ICNLP52887.2021.00036"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02503-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-022-02503-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-022-02503-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T10:12:02Z","timestamp":1688033522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-022-02503-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":35,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["2503"],"URL":"https:\/\/doi.org\/10.1007\/s00371-022-02503-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,11]]},"assertion":[{"value":"11 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article has no conflict of interest with any individual or organization.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Code and data are available.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code or data availability"}},{"value":"The experiments in this article are all realized through program operation, which will not cause harm to humans and animals and will not cause moral and ethical problems.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Welcome readers to communicate.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Completed at Hubei University of Technology on December 14, 2021.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}