{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:21:30Z","timestamp":1750220490420,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T00:00:00Z","timestamp":1634947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,23]]},"DOI":"10.1145\/3495018.3495034","type":"proceedings-article","created":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T17:33:51Z","timestamp":1647279231000},"page":"87-93","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Feature Fusion via 2D and 3D Networks for Action Recognition"],"prefix":"10.1145","author":[{"given":"Zijie","family":"Mo","sequence":"first","affiliation":[{"name":"Macau University of Science and Technology, China"}]},{"given":"Sio-Long","family":"Lo","sequence":"additional","affiliation":[{"name":"Macau University of Science and Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199","author":"Simonyan K.","year":"2014","unstructured":"Simonyan , K. , Zisserman , A. ( 2014 ). Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199 . Simonyan, K., Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. arXiv preprint arXiv:1406.2199."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00685"},{"key":"e_1_3_2_1_7_1","volume-title":"UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402","author":"Soomro K.","year":"2012","unstructured":"Soomro , K. , Zamir , A. R. , Shah , M. ( 2012 ). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 . Soomro, K., Zamir, A. R., Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402."},{"volume-title":"25th annual conference on neural information processing systems (NIPS 2011)","author":"Bergstra J.","key":"e_1_3_2_1_8_1","unstructured":"Bergstra , J. , Bardenet , R. , Bengio , Y. , K\u00e9gl , B. (2011 , December). Algorithms for hyper-parameter optimization . In 25th annual conference on neural information processing systems (NIPS 2011) Neural Information Processing Systems Foundation , vol. 24 . Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B. (2011, December). Algorithms for hyper-parameter optimization. In 25th annual conference on neural information processing systems (NIPS 2011) Neural Information Processing Systems Foundation, vol. 24."},{"key":"e_1_3_2_1_9_1","volume-title":"Practical bayesian optimization of machine learning algorithms. arXiv preprint arXiv:1206.2944","author":"Snoek J.","year":"2012","unstructured":"Snoek , J. , Larochelle , H. , Adams , R. P. ( 2012 ). Practical bayesian optimization of machine learning algorithms. arXiv preprint arXiv:1206.2944 . Snoek, J., Larochelle, H., Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. arXiv preprint arXiv:1206.2944."},{"key":"e_1_3_2_1_10_1","first-page":"265","article-title":"On the algorithmic implementation of multiclass kernel-based vector machines","volume":"2","author":"Crammer K.","year":"2001","unstructured":"Crammer , K. , Singer , Y. ( 2001 ). On the algorithmic implementation of multiclass kernel-based vector machines . Journal of machine learning research , 2 (Dec), pp. 265 - 292 . Crammer, K., Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of machine learning research, 2(Dec), pp. 265-292.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.441"},{"key":"e_1_3_2_1_14_1","first-page":"448","volume-title":"International conference on machine learning","author":"Ioffe S.","year":"2015","unstructured":"Ioffe , S. , Szegedy , C. ( 2015 , June). Batch normalization: Accelerating deep network training by reducing internal covariate shift . In International conference on machine learning , pp. 448 - 456 . Ioffe, S., Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448-456."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.590"},{"key":"e_1_3_2_1_16_1","first-page":"731","volume-title":"European Conference on Computer Vision","author":"Kalfaoglu M. E.","year":"2020","unstructured":"Kalfaoglu , M. E. , Kalkan , S. , Alatan , A. A. ( 2020 , August). Late temporal modeling in 3d cnn architectures with bert for action recognition . In European Conference on Computer Vision , pp. 731 - 747 . Kalfaoglu, M. E., Kalkan, S., Alatan, A. A. (2020, August). Late temporal modeling in 3d cnn architectures with bert for action recognition. In European Conference on Computer Vision, pp. 731-747."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964328"},{"key":"e_1_3_2_1_18_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan K.","year":"2014","unstructured":"Simonyan , K. , Zisserman , A. ( 2014 ). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 . Simonyan, K., Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"e_1_3_2_1_20_1","volume-title":"An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747","author":"Ruder S.","year":"2016","unstructured":"Ruder , S. ( 2016 ). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 . Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.373"},{"key":"e_1_3_2_1_22_1","volume-title":"Recognizing 50 human action categories of web videos. Machine vision and applications","author":"Reddy K. K.","year":"2013","unstructured":"Reddy , K. K. , Shah , M. ( 2013 ). Recognizing 50 human action categories of web videos. Machine vision and applications , vol. 24 , no. 5, pp. 971-981. Reddy, K. K., Shah, M. (2013). Recognizing 50 human action categories of web videos. Machine vision and applications, vol. 24, no. 5, pp. 971-981."},{"key":"e_1_3_2_1_23_1","volume-title":"The kinetics human action video dataset. arXiv preprint arXiv:1705.06950","author":"Kay W.","year":"2017","unstructured":"Kay , W. , Carreira , J. , Simonyan , K. , Zhang , B. , Hillier , C. , Vijayanarasimhan , S. , Zisserman , A. ( 2017 ). The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 . Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Zisserman, A. (2017). The kinetics human action video dataset. arXiv preprint arXiv:1705.06950."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/1763974.1764031"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"e_1_3_2_1_26_1","volume-title":"Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038","author":"Tran D.","year":"2017","unstructured":"Tran , D. , Ray , J. , Shou , Z. , Chang , S. F. , Paluri , M. ( 2017 ). Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038 . Tran, D., Ray, J., Shou, Z., Chang, S. F., Paluri, M. (2017). Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038."}],"event":{"name":"AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture","acronym":"AIAM2021","location":"Manchester United Kingdom"},"container-title":["2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495018.3495034","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3495018.3495034","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:19Z","timestamp":1750193359000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3495018.3495034"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,23]]},"references-count":26,"alternative-id":["10.1145\/3495018.3495034","10.1145\/3495018"],"URL":"https:\/\/doi.org\/10.1145\/3495018.3495034","relation":{},"subject":[],"published":{"date-parts":[[2021,10,23]]},"assertion":[{"value":"2022-03-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}