{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T12:30:35Z","timestamp":1742646635800,"version":"3.35.0"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"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":[[2025,1]]},"DOI":"10.1007\/s00371-024-03424-0","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T07:02:21Z","timestamp":1715583741000},"page":"1351-1366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rethinking group activity recognition under the open set condition"],"prefix":"10.1007","volume":"41","author":[{"given":"Liping","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Silin","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xianxiang","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Yixuan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"3424_CR1","doi-asserted-by":"crossref","unstructured":"Choi, W., Shahid, K., Savarese, S.: What are they doing?: Collective activity classification using spatio-temporal relationship among people. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1282\u20131289. IEEE (2009)","DOI":"10.1109\/ICCVW.2009.5457461"},{"key":"3424_CR2","doi-asserted-by":"publisher","first-page":"6386","DOI":"10.1109\/TMM.2024.3349923","volume":"26","author":"L Wu","year":"2024","unstructured":"Wu, L., Tian, M., Xiang, Y., Gu, K., Shi, G.: Learning label semantics for weakly supervised group activity recognition. IEEE Trans. Multimedia 26, 6386\u20136397 (2024)","journal-title":"IEEE Trans. Multimedia"},{"key":"3424_CR3","doi-asserted-by":"crossref","unstructured":"Wu, J., Wang, L., Wang, L., Guo, J., Wu, G.: Learning actor relation graphs for group activity recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9964\u20139974 (2019)","DOI":"10.1109\/CVPR.2019.01020"},{"key":"3424_CR4","doi-asserted-by":"publisher","first-page":"126646","DOI":"10.1016\/j.neucom.2023.126646","volume":"556","author":"L Wang","year":"2023","unstructured":"Wang, L., Feng, W., Tian, C., Chen, L., Pei, J.: 3d-unified spatial-temporal graph for group activity recognition. Neurocomputing 556, 126646 (2023)","journal-title":"Neurocomputing"},{"key":"3424_CR5","doi-asserted-by":"crossref","unstructured":"Li, S., Cao, Q., Liu, L., Yang, K., Liu, S., Hou, J., Yi, S.: Groupformer: Group activity recognition with clustered spatial-temporal transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13668\u201313677 (2021)","DOI":"10.1109\/ICCV48922.2021.01341"},{"key":"3424_CR6","doi-asserted-by":"crossref","unstructured":"Kim, D., Lee, J., Cho, M., Kwak, S.: Detector-free weakly supervised group activity recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20083\u201320093 (2022)","DOI":"10.1109\/CVPR52688.2022.01945"},{"key":"3424_CR7","doi-asserted-by":"crossref","unstructured":"Gavrilyuk, K., Sanford, R., Javan, M., Snoek, C.G.: Actor-transformers for group activity recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 839\u2013848 (2020)","DOI":"10.1109\/CVPR42600.2020.00092"},{"key":"3424_CR8","doi-asserted-by":"crossref","unstructured":"Zhou, H., Kadav, A., Shamsian, A., Geng, S., Lai, F., Zhao, L., Liu, T., Kapadia, M., Graf, H.P.: Composer: compositional reasoning of group activity in videos with keypoint-only modality. In: European Conference on Computer Vision, pp. 249\u2013266 (2022). Springer","DOI":"10.1007\/978-3-031-19833-5_15"},{"key":"3424_CR9","doi-asserted-by":"publisher","first-page":"104789","DOI":"10.1016\/j.imavis.2023.104789","volume":"137","author":"Z Du","year":"2023","unstructured":"Du, Z., Wang, X., Wang, Q.: Perceiving local relative motion and global correlations for weakly supervised group activity recognition. Image Vis. Comput. 137, 104789 (2023)","journal-title":"Image Vis. Comput."},{"key":"3424_CR10","doi-asserted-by":"crossref","unstructured":"Shu, Y., Shi, Y., Wang, Y., Zou, Y., Yuan, Q., Tian, Y.: Odn: Opening the deep network for open-set action recognition. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2018). IEEE","DOI":"10.1109\/ICME.2018.8486601"},{"key":"3424_CR11","doi-asserted-by":"publisher","first-page":"165997","DOI":"10.1109\/ACCESS.2019.2953455","volume":"7","author":"Y Yoon","year":"2019","unstructured":"Yoon, Y., Yu, J., Jeon, M.: Spatio-temporal representation matching-based open-set action recognition by joint learning of motion and appearance. IEEE Access 7, 165997\u2013166010 (2019)","journal-title":"IEEE Access"},{"key":"3424_CR12","doi-asserted-by":"crossref","unstructured":"Bao, W., Yu, Q., Kong, Y.: Evidential deep learning for open set action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13349\u201313358 (2021)","DOI":"10.1109\/ICCV48922.2021.01310"},{"key":"3424_CR13","doi-asserted-by":"crossref","unstructured":"Zhao, C., Du, D., Hoogs, A., Funk, C.: Open set action recognition via multi-label evidential learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22982\u201322991 (2023)","DOI":"10.1109\/CVPR52729.2023.02201"},{"issue":"6","key":"3424_CR14","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TPAMI.2013.220","volume":"36","author":"W Choi","year":"2013","unstructured":"Choi, W., Savarese, S.: Understanding collective activities of people from videos. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1242\u20131257 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3424_CR15","doi-asserted-by":"crossref","unstructured":"Shu, T., Xie, D., Rothrock, B., Todorovic, S., Chun\u00a0Zhu, S.: Joint inference of groups, events and human roles in aerial videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4576\u20134584 (2015)","DOI":"10.1109\/CVPR.2015.7299088"},{"issue":"11","key":"3424_CR16","doi-asserted-by":"publisher","first-page":"1980","DOI":"10.1109\/TCSVT.2013.2269780","volume":"23","author":"W Lin","year":"2013","unstructured":"Lin, W., Chu, H., Wu, J., Sheng, B., Chen, Z.: A heat-map-based algorithm for recognizing group activities in videos. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1980\u20131992 (2013)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"8","key":"3424_CR17","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1109\/TCSVT.2010.2057013","volume":"20","author":"W Lin","year":"2010","unstructured":"Lin, W., Sun, M.-T., Poovendran, R., Zhang, Z.: Group event detection with a varying number of group members for video surveillance. IEEE Trans. Circuits Syst. Video Technol. 20(8), 1057\u20131067 (2010)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3424_CR18","doi-asserted-by":"crossref","unstructured":"Amer, M.R., Xie, D., Zhao, M., Todorovic, S., Zhu, S.-C.: Cost-sensitive top-down\/bottom-up inference for multiscale activity recognition. In: Computer Vision\u2013ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV 12, pp. 187\u2013200 (2012). Springer","DOI":"10.1007\/978-3-642-33765-9_14"},{"key":"3424_CR19","doi-asserted-by":"crossref","unstructured":"Amer, M.R., Lei, P., Todorovic, S.: Hirf: Hierarchical random field for collective activity recognition in videos. In: European Conference on Computer Vision, pp. 572\u2013585 (2014). Springer","DOI":"10.1007\/978-3-319-10599-4_37"},{"key":"3424_CR20","doi-asserted-by":"crossref","unstructured":"Bagautdinov, T., Alahi, A., Fleuret, F., Fua, P., Savarese, S.: Social scene understanding: End-to-end multi-person action localization and collective activity recognition. IEEE Conference on Computer Vision & Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2017.365"},{"key":"3424_CR21","doi-asserted-by":"crossref","unstructured":"Ibrahim, M.S., Muralidharan, S., Deng, Z., Vahdat, A., Mori, G.: A hierarchical deep temporal model for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971\u20131980 (2016)","DOI":"10.1109\/CVPR.2016.217"},{"key":"3424_CR22","doi-asserted-by":"crossref","unstructured":"Shu, T., Todorovic, S., Zhu, S.-C.: Cern: confidence-energy recurrent network for group activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5523\u20135531 (2017)","DOI":"10.1109\/CVPR.2017.453"},{"key":"3424_CR23","doi-asserted-by":"crossref","unstructured":"Wang, M., Ni, B., Yang, X.: Recurrent modeling of interaction context for collective activity recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3048\u20133056 (2017)","DOI":"10.1109\/CVPR.2017.783"},{"key":"3424_CR24","doi-asserted-by":"crossref","unstructured":"Tang, Y., Wang, Z., Li, P., Lu, J., Yang, M., Zhou, J.: Mining semantics-preserving attention for group activity recognition. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 1283\u20131291 (2018)","DOI":"10.1145\/3240508.3240576"},{"key":"3424_CR25","doi-asserted-by":"crossref","unstructured":"Qi, M., Jie, Q., Li, A., Wang, Y., Luo, J., Gool, L.V.: stagnet: An attentive semantic rnn for group activity recognition. In: Springer, Cham (2018)","DOI":"10.1007\/978-3-030-01249-6_7"},{"issue":"2","key":"3424_CR26","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1109\/TPAMI.2019.2928540","volume":"44","author":"J Tang","year":"2019","unstructured":"Tang, J., Shu, X., Yan, R., Zhang, L.: Coherence constrained graph LSTM for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 636\u2013647 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"3424_CR27","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TPAMI.2019.2942030","volume":"43","author":"X Shu","year":"2019","unstructured":"Shu, X., Tang, J., Qi, G.-J., Liu, W., Yang, J.: Hierarchical long short-term concurrent memory for human interaction recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 1110\u20131118 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3424_CR28","doi-asserted-by":"crossref","unstructured":"Ehsanpour, M., Abedin, A., Saleh, F., Shi, J., Reid, I., Rezatofighi, H.: Joint learning of social groups, individuals action and sub-group activities in videos. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IX 16, pp. 177\u2013195 (2020). Springer","DOI":"10.1007\/978-3-030-58545-7_11"},{"key":"3424_CR29","doi-asserted-by":"crossref","unstructured":"Yuan, H., Ni, D., Wang, M.: Spatio-temporal dynamic inference network for group activity recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7476\u20137485 (2021)","DOI":"10.1109\/ICCV48922.2021.00738"},{"issue":"5","key":"3424_CR30","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/TCSVT.2013.2280849","volume":"24","author":"W Lin","year":"2013","unstructured":"Lin, W., Chen, Y., Wu, J., Wang, H., Sheng, B., Li, H.: A new network-based algorithm for human activity recognition in videos. IEEE Trans. Circuits Syst. Video Technol. 24(5), 826\u2013841 (2013)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"3424_CR31","doi-asserted-by":"publisher","first-page":"6955","DOI":"10.1109\/TPAMI.2020.3034233","volume":"45","author":"R Yan","year":"2020","unstructured":"Yan, R., Xie, L., Tang, J., Shu, X., Tian, Q.: Higcin: Hierarchical graph-based cross inference network for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 6955\u20136968 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3424_CR32","doi-asserted-by":"crossref","unstructured":"Pramono, R.R.A., Chen, Y.T., Fang, W.H.: Empowering relational network by self-attention augmented conditional random fields for group activity recognition. In: European Conference on Computer Vision, pp. 71\u201390 (2020). Springer","DOI":"10.1007\/978-3-030-58452-8_5"},{"key":"3424_CR33","doi-asserted-by":"crossref","unstructured":"Yan, R., Xie, L., Tang, J., Shu, X., Tian, Q.: Social adaptive module for weakly-supervised group activity recognition. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VIII 16, pp. 208\u2013224 (2020). Springer","DOI":"10.1007\/978-3-030-58598-3_13"},{"key":"3424_CR34","doi-asserted-by":"crossref","unstructured":"Hu, G., Cui, B., He, Y., Yu, S.: Progressive relation learning for group activity recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 980\u2013989 (2020)","DOI":"10.1109\/CVPR42600.2020.00106"},{"issue":"9","key":"3424_CR35","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TCSVT.2020.2973301","volume":"30","author":"Y Tang","year":"2020","unstructured":"Tang, Y., Wei, Y., Yu, X., Lu, J., Zhou, J.: Graph interaction networks for relation transfer in human activity videos. IEEE Trans. Circuits Syst. Video Technol. 30(9), 2872\u20132886 (2020)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"3424_CR36","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1109\/TNNLS.2020.2978942","volume":"32","author":"X Shu","year":"2020","unstructured":"Shu, X., Zhang, L., Sun, Y., Tang, J.: Host-parasite: graph LSTM-in-LSTM for group activity recognition. IEEE Trans Neural Netw Learn Syst 32(2), 663\u2013674 (2020)","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3424_CR37","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Adv Neural Inf Process Syst 30 (2017)"},{"key":"3424_CR38","unstructured":"Tarashima, S., Center, I.: One-shot deep model for end-to-end multi-person activity recognition. In: British Machine Vision Conference (2021)"},{"key":"3424_CR39","doi-asserted-by":"crossref","unstructured":"Yuan, H., Ni, D.: Learning visual context for group activity recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence 35, 3261\u20133269 (2021)","DOI":"10.1609\/aaai.v35i4.16437"},{"key":"3424_CR40","doi-asserted-by":"crossref","unstructured":"Li, W., Yang, T., Wu, X., Du, X.-J., Qiao, J.-J.: Learning action-guided spatio-temporal transformer for group activity recognition. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 2051\u20132060 (2022)","DOI":"10.1145\/3503161.3547825"},{"key":"3424_CR41","doi-asserted-by":"crossref","unstructured":"Hu, B., Cham, T.-J.: Entry-flipped transformer for inference and prediction of participant behavior. In: European Conference on Computer Vision, pp. 439\u2013456 (2022). Springer","DOI":"10.1007\/978-3-031-19772-7_26"},{"key":"3424_CR42","doi-asserted-by":"crossref","unstructured":"Han, M., Zhang, D.J., Wang, Y., Yan, R., Yao, L., Chang, X., Qiao, Y.: Dual-ai: Dual-path actor interaction learning for group activity recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2990\u20132999 (2022)","DOI":"10.1109\/CVPR52688.2022.00300"},{"key":"3424_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, X., Zhou, Y., Wang, D., Ouyang, W., Su, R.: Mlst-former: Multi-level spatial-temporal transformer for group activity recognition. IEEE Transactions on Circuits and Systems for Video Technology (2022)","DOI":"10.1109\/TCSVT.2022.3233069"},{"key":"3424_CR44","doi-asserted-by":"publisher","first-page":"5076","DOI":"10.1109\/TCSVT.2023.3249906","volume":"33","author":"Z Du","year":"2023","unstructured":"Du, Z., Wang, X., Wang, Q.: Self-supervised global spatio-temporal interaction pre-training for group activity recognition. IEEE Trans. Circuits Syst. Video Technol. 33, 5076\u20135088 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"11","key":"3424_CR45","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1109\/TPAMI.2005.224","volume":"27","author":"F Li","year":"2005","unstructured":"Li, F., Wechsler, H.: Open set face recognition using transduction. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1686\u20131697 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"3424_CR46","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1109\/TPAMI.2012.256","volume":"35","author":"WJ Scheirer","year":"2012","unstructured":"Scheirer, W.J., Rezende Rocha, A., Sapkota, A., Boult, T.E.: Toward open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1757\u20131772 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3424_CR47","doi-asserted-by":"crossref","unstructured":"Jain, L.P., Scheirer, W.J., Boult, T.E.: Multi-class open set recognition using probability of inclusion. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III 13, pp. 393\u2013409 (2014). Springer","DOI":"10.1007\/978-3-319-10578-9_26"},{"key":"3424_CR48","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563\u20131572 (2016)","DOI":"10.1109\/CVPR.2016.173"},{"key":"3424_CR49","doi-asserted-by":"crossref","unstructured":"Neal, L., Olson, M., Fern, X., Wong, W.-K., Li, F.: Open set learning with counterfactual images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 613\u2013628 (2018)","DOI":"10.1007\/978-3-030-01231-1_38"},{"key":"3424_CR50","doi-asserted-by":"crossref","unstructured":"Ditria, L., Meyer, B.J., Drummond, T.: Opengan: Open set generative adversarial networks. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69538-5_29"},{"issue":"24","key":"3424_CR51","doi-asserted-by":"publisher","first-page":"4725","DOI":"10.3390\/math10244725","volume":"10","author":"G Yang","year":"2022","unstructured":"Yang, G., Zhou, S., Wan, M.: Open-set recognition model based on negative-class sample feature enhancement learning algorithm. Mathematics 10(24), 4725 (2022)","journal-title":"Mathematics"},{"key":"3424_CR52","doi-asserted-by":"crossref","unstructured":"Yoshihashi, R., Shao, W., Kawakami, R., You, S., Iida, M., Naemura, T.: Classification-reconstruction learning for open-set recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4016\u20134025 (2019)","DOI":"10.1109\/CVPR.2019.00414"},{"key":"3424_CR53","doi-asserted-by":"publisher","first-page":"120063","DOI":"10.1109\/ACCESS.2022.3222310","volume":"10","author":"H Oh","year":"2022","unstructured":"Oh, H., Kim, S.B.: Multivariate time series open-set recognition using multi-feature extraction and reconstruction. IEEE Access 10, 120063\u2013120073 (2022)","journal-title":"IEEE Access"},{"issue":"4","key":"3424_CR54","first-page":"4214","volume":"45","author":"H Huang","year":"2022","unstructured":"Huang, H., Wang, Y., Hu, Q., Cheng, M.-M.: Class-specific semantic reconstruction for open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4214\u20134228 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3424_CR55","unstructured":"Roitberg, A., Al-Halah, Z., Stiefelhagen, R.: Informed democracy: voting-based novelty detection for action recognition. arXiv preprint arXiv:1810.12819 (2018)"},{"key":"3424_CR56","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"3424_CR57","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"issue":"10","key":"3424_CR58","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349\u20133364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3424_CR59","unstructured":"Choi, J., Gao, C., Messou, J.C., Huang, J.-B.: Why can\u2019t I dance in the mall? Learning to mitigate scene bias in action recognition. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"3424_CR60","first-page":"35710","volume":"35","author":"Y-W Kim","year":"2022","unstructured":"Kim, Y.-W., Mishra, S., Jin, S., Panda, R., Kuehne, H., Karlinsky, L., Saligrama, V., Saenko, K., Oliva, A., Feris, R.: How transferable are video representations based on synthetic data? Adv. Neural Inf. Process. Syst. 35, 35710\u201335723 (2022)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"3424_CR61","doi-asserted-by":"crossref","unstructured":"Duan, H., Zhao, Y., Chen, K., Lin, D., Dai, B.: Revisiting skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2969\u20132978 (2022)","DOI":"10.1109\/CVPR52688.2022.00298"},{"key":"3424_CR62","doi-asserted-by":"crossref","unstructured":"Noor, N., Park, I.K.: A lightweight skeleton-based 3d-cnn for real-time fall detection and action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2179\u20132188 (2023)","DOI":"10.1109\/ICCVW60793.2023.00232"},{"key":"3424_CR63","doi-asserted-by":"crossref","unstructured":"Zhai, X., Hu, Z., Yang, D., Zhou, L., Liu, J.: Spatial temporal network for image and skeleton based group activity recognition. In: Proceedings of the Asian Conference on Computer Vision, pp. 20\u201338 (2022)","DOI":"10.1007\/978-3-031-26316-3_20"},{"key":"3424_CR64","doi-asserted-by":"crossref","unstructured":"Zhang, J., Jia, Y., Xie, W., Tu, Z.: Zoom transformer for skeleton-based group activity recognition. IEEE Trans. Circuits Syst. Video Technol. 32(12), 8646\u20138659 (2022)","DOI":"10.1109\/TCSVT.2022.3193574"},{"key":"3424_CR65","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucom.2022.09.071","volume":"512","author":"R Yue","year":"2022","unstructured":"Yue, R., Tian, Z., Du, S.: Action recognition based on RGB and skeleton data sets: a survey. Neurocomputing 512, 287\u2013306 (2022)","journal-title":"Neurocomputing"},{"key":"3424_CR66","doi-asserted-by":"crossref","unstructured":"Yan, R., Tang, J., Shu, X., Li, Z., Tian, Q.: Participation-contributed temporal dynamic model for group activity recognition. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 1292\u20131300 (2018)","DOI":"10.1145\/3240508.3240572"},{"key":"3424_CR67","unstructured":"Li, D., Xie, Y., Zhang, W., Tang, Y., Zhang, Z.: Attentive pooling for group activity recognition. arXiv preprint arXiv:2208.14847 (2022)"},{"issue":"1","key":"3424_CR68","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1007\/s10489-022-03470-y","volume":"53","author":"K Mao","year":"2023","unstructured":"Mao, K., Jin, P., Ping, Y., Tang, B.: Modeling multi-scale sub-group context for group activity recognition. Appl. Intell. 53(1), 1149\u20131161 (2023)","journal-title":"Appl. Intell."},{"key":"3424_CR69","doi-asserted-by":"publisher","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","volume":"8","author":"KP Sinaga","year":"2020","unstructured":"Sinaga, K.P., Yang, M.-S.: Unsupervised k-means clustering algorithm. IEEE Access 8, 80716\u201380727 (2020)","journal-title":"IEEE Access"},{"key":"3424_CR70","doi-asserted-by":"crossref","unstructured":"Sentz, K., Ferson, S.: Combination of evidence in dempster-shafer theory (2002)","DOI":"10.2172\/800792"},{"key":"3424_CR71","doi-asserted-by":"crossref","unstructured":"J\u00f8sang, A.: Subjective logic (2016)","DOI":"10.1007\/978-3-319-42337-1"},{"key":"3424_CR72","doi-asserted-by":"crossref","unstructured":"Yang, K., Gao, J., Feng, Y., Xu, C.: Leveraging attribute knowledge for open-set action recognition. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp. 762\u2013767 (2023). IEEE","DOI":"10.1109\/ICME55011.2023.00136"},{"key":"3424_CR73","doi-asserted-by":"crossref","unstructured":"Ibrahim, M., Muralidharan, S., Deng, Z., Vahdat, A., Mori, G.: A hierarchical deep temporal model for group activity recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.217"},{"key":"3424_CR74","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)"},{"key":"3424_CR75","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606 (2018)"},{"key":"3424_CR76","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"3424_CR77","doi-asserted-by":"crossref","unstructured":"Chen, G., Qiao, L., Shi, Y., Peng, P., Li, J., Huang, T., Pu, S., Tian, Y.: Learning open set network with discriminative reciprocal points. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part III 16, pp. 507\u2013522 (2020). Springer","DOI":"10.1007\/978-3-030-58580-8_30"},{"key":"3424_CR78","unstructured":"Krishnan, R., Subedar, M., Tickoo, O.: Bar: Bayesian activity recognition using variational inference. arXiv preprint arXiv:1811.03305 (2018)"},{"key":"3424_CR79","doi-asserted-by":"publisher","first-page":"129230","DOI":"10.1109\/ACCESS.2023.3332651","volume":"11","author":"C Wang","year":"2023","unstructured":"Wang, C., Mohamed, A.S.A.: Attention relational network for skeleton-based group activity recognition. IEEE Access 11, 129230\u2013129239 (2023)","journal-title":"IEEE Access"},{"issue":"10","key":"3424_CR80","doi-asserted-by":"publisher","first-page":"15515","DOI":"10.1007\/s11042-022-13867-z","volume":"82","author":"Y Li","year":"2023","unstructured":"Li, Y., Liu, Y., Yu, R., Zong, H., Xie, W.: Dual attention based spatial-temporal inference network for volleyball group activity recognition. Multimedia Tools Appl. 82(10), 15515\u201315533 (2023)","journal-title":"Multimedia Tools Appl."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03424-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03424-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03424-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:39:16Z","timestamp":1738586356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03424-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":80,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["3424"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03424-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"21 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}