{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:45:41Z","timestamp":1791463541260,"version":"4.3.4"},"reference-count":147,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1513126"],"award-info":[{"award-number":["1513126"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1956050"],"award-info":[{"award-number":["1956050"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004281","name":"Polish National Science Centre","doi-asserted-by":"crossref","award":["2011\/01\/M\/NZ8\/03344"],"award-info":[{"award-number":["2011\/01\/M\/NZ8\/03344"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004281","name":"Polish National Science Centre","doi-asserted-by":"crossref","award":["2018\/29\/B\/NZ8\/02312"],"award-info":[{"award-number":["2018\/29\/B\/NZ8\/02312"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Advances in visual perceptual tasks have been mainly driven by the amount, and types, of annotations of large-scale datasets. Researchers have focused on fully-supervised settings to train models using offline epoch-based schemes. Despite the evident advancements, limitations and cost of manually annotated datasets have hindered further development for event perceptual tasks, such as detection and localization of objects and events in videos. The problem is more apparent in zoological applications due to the scarcity of annotations and length of videos-most videos are at most ten minutes long. Inspired by cognitive theories, we present a self-supervised perceptual prediction framework to tackle the problem of temporal event segmentation by building a stable representation of event-related objects. The approach is simple but effective. We rely on LSTM predictions of high-level features computed by a standard deep learning backbone. For spatial segmentation, the stable representation of the object is used by an attention mechanism to filter the input features before the prediction step. The self-learned attention maps effectively localize the object as a side effect of perceptual prediction. We demonstrate our approach on long videos from continuous wildlife video monitoring, spanning multiple days at 25 FPS. We aim to facilitate automated ethogramming by detecting and localizing events without the need for labels. Our approach is trained in an online manner on streaming input and requires only a single pass through the video, with no separate training set. Given the lack of long and realistic (includes real-world challenges) datasets, we introduce a new wildlife video dataset\u2013nest monitoring of the Kagu (a flightless bird from New Caledonia)\u2013to benchmark our approach. Our dataset features a video from 10 days (over 23 million frames) of continuous monitoring of the Kagu in its natural habitat. We annotate every frame with bounding boxes and event labels. Additionally, each frame is annotated with time-of-day and illumination conditions. We will make the dataset, which is the first of its kind, and the code available to the research community. We find that the approach significantly outperforms other self-supervised, traditional (e.g., Optical Flow, Background Subtraction) and NN-based (e.g., PA-DPC, DINO, iBOT), baselines and performs on par with supervised boundary detection approaches (i.e., PC). At a recall rate of 80%, our best performing model detects one false positive activity every 50\u00a0min of training. On average, we at least double the performance of self-supervised approaches for spatial segmentation. Additionally, we show that our approach is robust to various environmental conditions (e.g., moving shadows). We also benchmark the framework on other datasets (i.e., Kinetics-GEBD, TAPOS) from different domains to demonstrate its generalizability. The data and code are available on our project page: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/aix.eng.usf.edu\/research_automated_ethogramming.html\">https:\/\/aix.eng.usf.edu\/research_automated_ethogramming.html<\/jats:ext-link><\/jats:p>","DOI":"10.1007\/s11263-023-01781-2","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T07:02:33Z","timestamp":1682665353000},"page":"2267-2297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Towards Automated Ethogramming: Cognitively-Inspired Event Segmentation for Streaming Wildlife Video Monitoring"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6296-0793","authenticated-orcid":false,"given":"Ramy","family":"Mounir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5916-6262","authenticated-orcid":false,"given":"Ahmed","family":"Shahabaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0619-5123","authenticated-orcid":false,"given":"Roman","family":"Gula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7273-3073","authenticated-orcid":false,"given":"J\u00f6rn","family":"Theuerkauf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7332-4207","authenticated-orcid":false,"given":"Sudeep","family":"Sarkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"1781_CR1","doi-asserted-by":"crossref","unstructured":"Aakur, S. N., & Sarkar, S. (2019). A perceptual prediction framework for self supervised event segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1197\u20131206).","DOI":"10.1109\/CVPR.2019.00129"},{"key":"1781_CR2","doi-asserted-by":"crossref","unstructured":"Aakur, S., & Sarkar, S. (2020). Action localization through continual predictive learning. In European conference on computer vision (pp. 300\u2013317). Springer","DOI":"10.1007\/978-3-030-58568-6_18"},{"key":"1781_CR3","unstructured":"ActEV: Activities in Extended Video. https:\/\/actev.nist.gov\/"},{"key":"1781_CR4","doi-asserted-by":"crossref","unstructured":"Adeli, V., Ehsanpour, M., Reid, I., Niebles, J.C., Savarese, S., Adeli, E., & Rezatofighi, H. (2021). Tripod: Human trajectory and pose dynamics forecasting in the wild. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV48922.2021.01314"},{"issue":"7","key":"1781_CR5","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.3390\/ani10071207","volume":"10","author":"HG Ak\u00e7ay","year":"2020","unstructured":"Ak\u00e7ay, H. G., Kabasakal, B., Aksu, D., Demir, N., \u00d6z, M., & Erdo\u011fan, A. (2020). Automated bird counting with deep learning for regional bird distribution mapping. Animals, 10(7), 1207.","journal-title":"Animals"},{"key":"1781_CR6","doi-asserted-by":"crossref","unstructured":"Alayrac, J.-B., Bojanowski, P., Agrawal, N., Sivic, J., Laptev, I., & Lacoste-Julien, S. (2016). Unsupervised learning from narrated instruction videos. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4575\u20134583).","DOI":"10.1109\/CVPR.2016.495"},{"key":"1781_CR7","doi-asserted-by":"crossref","unstructured":"Apostolidis, E., Balaouras, G., Mezaris, V., & Patras, I. (2021). Combining global and local attention with positional encoding for video summarization. In 2021 IEEE international symposium on multimedia (ISM) (pp. 226\u2013234). IEEE","DOI":"10.1109\/ISM52913.2021.00045"},{"key":"1781_CR8","doi-asserted-by":"crossref","unstructured":"Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lu\u010di\u0107, M., & Schmid, C. (2021). Vivit: A video vision transformer. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV) (pp. 6836\u20136846).","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"1781_CR9","unstructured":"Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"key":"1781_CR10","unstructured":"Bardes, A., Ponce, J., & LeCun, Y. (2022). VICReg: Variance-invariance-covariance regularization for self-supervised learning. In International conference on learning representations."},{"key":"1781_CR11","doi-asserted-by":"crossref","unstructured":"Bencz\u00far, A.A., Kocsis, L., & P\u00e1lovics, R. (2018). Online machine learning in big data streams. arXiv preprint arXiv:1802.05872","DOI":"10.1007\/978-3-319-63962-8_326-1"},{"key":"1781_CR12","doi-asserted-by":"crossref","unstructured":"Bhatnagar, B.L., Singh, S., Arora, C., & Jawahar, C. (2017). CVIT, K. Unsupervised learning of deep feature representation for clustering egocentric actions. In IJCAI (pp. 1447\u20131453).","DOI":"10.24963\/ijcai.2017\/200"},{"key":"1781_CR13","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C., & Sivic, J. (2014). Weakly supervised action labeling in videos under ordering constraints. In European conference on computer vision (pp. 628\u2013643). Springer","DOI":"10.1007\/978-3-319-10602-1_41"},{"key":"1781_CR14","doi-asserted-by":"crossref","unstructured":"Bondi, E., Jain, R., Aggrawal, P., Anand, S., Hannaford, R., Kapoor, A., Piavis, J., Shah, S., Joppa, L., Dilkina, B., & Tambe, M. (2020). Birdsai: A dataset for detection and tracking in aerial thermal infrared videos. In WACV.","DOI":"10.1109\/WACV45572.2020.9093284"},{"key":"1781_CR15","unstructured":"Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language models are few-shot learners. In Advances in neural information processing systems (vol. 33) (NeurIPS)"},{"key":"1781_CR16","doi-asserted-by":"publisher","unstructured":"Burgos-Artizzu, X. P., Doll\u00e1r, P., Lin, D., Anderson, D. J., & Perona, P. (2012). Social behavior recognition in continuous video. In 2012 IEEE conference on computer vision and pattern recognition (pp. 1322\u20131329). https:\/\/doi.org\/10.1109\/CVPR.2012.6247817","DOI":"10.1109\/CVPR.2012.6247817"},{"key":"1781_CR17","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European conference on computer vision (pp. 213\u2013229). Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1781_CR18","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"1781_CR19","doi-asserted-by":"crossref","unstructured":"Chen, X., & He, K. (2021). Exploring simple siamese representation learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 15750\u201315758).","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"1781_CR20","doi-asserted-by":"crossref","unstructured":"Chen, Z., Xie, L., Niu, J., Liu, X., Wei, L., & Tian, Q. (2021). Visformer: The vision-friendly transformer. In Proceedings of the IEEE\/CVF international conference on computer vision.","DOI":"10.1109\/ICCV48922.2021.00063"},{"key":"1781_CR21","doi-asserted-by":"crossref","unstructured":"Choi, Y., El-Khamy, M., & Lee, J. (2021). Dual-teacher class-incremental learning with data-free generative replay. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops (pp. 3543\u20133552).","DOI":"10.1109\/CVPRW53098.2021.00393"},{"issue":"23","key":"1781_CR22","doi-asserted-by":"publisher","first-page":"12883","DOI":"10.1002\/ece3.6840","volume":"10","author":"M Clapham","year":"2020","unstructured":"Clapham, M., Miller, E., Nguyen, M., & Darimont, C. T. (2020). Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears. Ecology and Evolution, 10(23), 12883\u201312892.","journal-title":"Ecology and Evolution"},{"key":"1781_CR23","doi-asserted-by":"crossref","unstructured":"Corona, K., Osterdahl, K., Collins, R., & Hoogs, A. (2021). Meva: A large-scale multiview, multimodal video dataset for activity detection. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision (pp. 1060\u20131068).","DOI":"10.1109\/WACV48630.2021.00110"},{"key":"1781_CR24","unstructured":"Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"},{"key":"1781_CR25","unstructured":"Ding, L., & Xu, C. (2018). Weakly-supervised action segmentation with iterative soft boundary assignment. In IEEE\/CVF conference on computer vision and pattern recognition."},{"key":"1781_CR26","unstructured":"Ding, L., & Xu, C. (2018). Weakly-supervised action segmentation with iterative soft boundary assignment. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6508\u20136516)."},{"key":"1781_CR27","doi-asserted-by":"crossref","unstructured":"Doersch, C., Gupta, A., & Efros, A. A. (2015) Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision (pp. 1422\u20131430).","DOI":"10.1109\/ICCV.2015.167"},{"key":"1781_CR28","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et\u00a0al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. In International conference on learning representations."},{"key":"1781_CR29","doi-asserted-by":"crossref","unstructured":"Dwivedi, S. K., Athanasiou, N., Kocabas, M., & Black, M. J. (2021). Learning to regress bodies from images using differentiable semantic rendering. In Proceedings of international conference on computer vision (ICCV) (pp. 11250\u201311259).","DOI":"10.1109\/ICCV48922.2021.01106"},{"issue":"2","key":"1781_CR30","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2), 303\u2013338.","journal-title":"International Journal of Computer Vision"},{"key":"1781_CR31","doi-asserted-by":"crossref","unstructured":"Fajtl, J., Sokeh, H. S., Argyriou, V., Monekosso, D., & Remagnino, P. (2018). Summarizing videos with attention. In Asian conference on computer vision (pp. 39\u201354). Springer","DOI":"10.1007\/978-3-030-21074-8_4"},{"key":"1781_CR32","unstructured":"Fangbemi, A.S., Lu, Y.F., Xu, M.Y., Luo, X.W., Rolland, A., & Raissi, C. (2020). Zoobuilder: 2D and 3D pose estimation for quadrupeds using synthetic data. arXiv preprint arXiv:2009.05389"},{"key":"1781_CR33","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., & Weiss, Y. (eds.) (2018). Computer Vision-ECCV 2018-15th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part XVI. Lecture Notes in Computer Science","DOI":"10.1007\/978-3-030-01270-0"},{"issue":"9","key":"1781_CR34","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1111\/2041-210X.13436","volume":"11","author":"AC Ferreira","year":"2020","unstructured":"Ferreira, A. C., Silva, L. R., Renna, F., Brandl, H. B., Renoult, J. P., Farine, D. R., Covas, R., & Doutrelant, C. (2020). Deep learning-based methods for individual recognition in small birds. Methods in Ecology and Evolution, 11(9), 1072\u20131085.","journal-title":"Methods in Ecology and Evolution"},{"key":"1781_CR35","unstructured":"Finn, C., Goodfellow, I., & Levine, S. (2016). Unsupervised learning for physical interaction through video prediction. In Advances in neural information processing systems (pp. 64\u201372)."},{"key":"1781_CR36","unstructured":"Garcia\u00a0del Molino, A., Lim, J.-H., Tan, A.-H.: Predicting visual context for unsupervised event segmentation in continuous photo-streams. In Proceedings of the 26th ACM international conference on multimedia."},{"key":"1781_CR37","doi-asserted-by":"crossref","unstructured":"Girdhar, R., Carreira, J., Doersch, C., & Zisserman, A. (2019). Video action transformer network. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 244\u2013253)","DOI":"10.1109\/CVPR.2019.00033"},{"key":"1781_CR38","first-page":"21271","volume":"33","author":"J-B Grill","year":"2020","unstructured":"Grill, J.-B., Strub, F., Altch\u00e9, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al. (2020). Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems, 33, 21271\u201321284.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1781_CR39","doi-asserted-by":"crossref","unstructured":"Guan, T., Wang, J., Lan, S., Chandra, R., Wu, Z., Davis, L., & Manocha, D. (2022). M3detr: Multi-representation, multi-scale, mutual-relation 3D object detection with transformers. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision.","DOI":"10.1109\/WACV51458.2022.00235"},{"key":"1781_CR40","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s10344-010-0392-y","volume":"56","author":"R Gula","year":"2010","unstructured":"Gula, R., Theuerkauf, J., Rouys, S., & Legault, A. (2010). An audio\/video surveillance system for wildlife. European Journal of Wildlife Research, 56, 803\u2013807. https:\/\/doi.org\/10.1007\/s10344-010-0392-y","journal-title":"European Journal of Wildlife Research"},{"key":"1781_CR41","doi-asserted-by":"crossref","unstructured":"G\u00fcnel, S., Rhodin, H., Morales, D., Campagnolo, J., Ramdya, P., & Fua, P. (2019). Deepfly 3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila. eLife","DOI":"10.7554\/eLife.48571.033"},{"key":"1781_CR42","doi-asserted-by":"crossref","unstructured":"Han, T., Xie, W., & Zisserman, A. (2019). Video representation learning by dense predictive coding. In Proceedings of the IEEE\/CVF international conference on computer vision workshops (pp. 0\u20130).","DOI":"10.1109\/ICCVW.2019.00186"},{"key":"1781_CR43","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fncir.2016.00023","volume":"10","author":"J Hawkins","year":"2016","unstructured":"Hawkins, J., & Ahmad, S. (2016). Why neurons have thousands of synapses, a theory of sequence memory in neocortex. Frontiers in Neural Circuits, 10, 23.","journal-title":"Frontiers in Neural Circuits"},{"key":"1781_CR44","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3389\/fncir.2017.00081","volume":"11","author":"J Hawkins","year":"2017","unstructured":"Hawkins, J., Ahmad, S., & Cui, Y. (2017). A theory of how columns in the neocortex enable learning the structure of the world. Frontiers in Neural Circuits, 11, 81.","journal-title":"Frontiers in Neural Circuits"},{"issue":"8","key":"1781_CR45","doi-asserted-by":"publisher","first-page":"0236925","DOI":"10.1371\/journal.pone.0236925","volume":"15","author":"WD Hawkins","year":"2020","unstructured":"Hawkins, W. D., & DuRant, S. E. (2020). Applications of machine learning in behavioral ecology: Quantifying avian incubation behavior and nest conditions in relation to environmental temperature. PLoS ONE, 15(8), 0236925.","journal-title":"PLoS ONE"},{"key":"1781_CR46","first-page":"348","volume":"23","author":"BY Hayden","year":"2021","unstructured":"Hayden, B. Y., Park, H. S., & Zimmermann, J. (2021). Automated pose estimation in primates. American Journal of Primatology, 23, 348.","journal-title":"American Journal of Primatology"},{"issue":"10","key":"1781_CR47","doi-asserted-by":"publisher","first-page":"2540","DOI":"10.1109\/TPAMI.2019.2917908","volume":"42","author":"N Hesse","year":"2020","unstructured":"Hesse, N., Pujades, S., Black, M. J., Arens, M., Hofmann, U., & Schroeder, S. (2020). Learning and tracking the 3D body shape of freely moving infants from RGB-D sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 42(10), 2540\u20132551. https:\/\/doi.org\/10.1109\/TPAMI.2019.2917908","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)"},{"issue":"8","key":"1781_CR48","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"issue":"1","key":"1781_CR49","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3354\/esr00186","volume":"7","author":"J Holmberg","year":"2009","unstructured":"Holmberg, J., Norman, B., & Arzoumanian, Z. (2009). Estimating population size, structure, and residency time for whale sharks rhincodon typus through collaborative photo-identification. Endangered Species Research, 7(1), 39\u201353.","journal-title":"Endangered Species Research"},{"key":"1781_CR50","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bi, H., Li, Z., Mao, T., & Wang, Z. (2019). Stgat: Modeling spatial-temporal interactions for human trajectory prediction. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 6272\u20136281).","DOI":"10.1109\/ICCV.2019.00637"},{"key":"1781_CR51","doi-asserted-by":"crossref","unstructured":"Huang, D.-A., Fei-Fei, L., & Niebles, J. C. (2016). Connectionist temporal modeling for weakly supervised action labeling. In ECCV.","DOI":"10.1007\/978-3-319-46493-0_9"},{"key":"1781_CR52","doi-asserted-by":"crossref","unstructured":"Huang, D.-A., Fei-Fei, L., & Niebles, J. C. (2016). Connectionist temporal modeling for weakly supervised action labeling. In European conference on computer vision (pp. 137\u2013153). Springer","DOI":"10.1007\/978-3-319-46493-0_9"},{"key":"1781_CR53","doi-asserted-by":"crossref","unstructured":"Ivanovic, B., & Pavone, M. (2019). The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 2375\u20132384).","DOI":"10.1109\/ICCV.2019.00246"},{"issue":"6","key":"1781_CR54","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1109\/TCSVT.2019.2904996","volume":"30","author":"Z Ji","year":"2019","unstructured":"Ji, Z., Xiong, K., Pang, Y., & Li, X. (2019). Video summarization with attention-based encoder-decoder networks. IEEE Transactions on Circuits and Systems for Video Technology, 30(6), 1709\u20131717.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"1781_CR55","doi-asserted-by":"crossref","unstructured":"Kamath, A., Singh, M., LeCun, Y., Misra, I., Synnaeve, G., & Carion, N. (2021). Mdetr-modulated detection for end-to-end multi-modal understanding. In 2021 IEEE\/CVF international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV48922.2021.00180"},{"issue":"2","key":"1781_CR56","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1111\/cgf.12838","volume":"35","author":"A Kanazawa","year":"2016","unstructured":"Kanazawa, A., Kovalsky, S., Basri, R., & Jacobs, D. (2016). Learning 3D deformation of animals from 2d images. Computer Graphics Forum, 35(2), 365\u2013374. https:\/\/doi.org\/10.1111\/cgf.12838","journal-title":"Computer Graphics Forum"},{"key":"1781_CR57","unstructured":"Kang, H., Kim, J., Kim, K., Kim, T., & Kim, S. J. (2021). Winning the cvpr\u20192021 kinetics-gebd challenge: Contrastive learning approach. arXiv preprint arXiv:2106.11549"},{"key":"1781_CR58","doi-asserted-by":"crossref","unstructured":"Kellenberger, B., Veen, T., Folmer, E., & Tuia, D. (2021). 21 000 birds in 4.5 h: Efficient large-scale seabird detection with machine learning. Remote Sensing in Ecology and Conservation","DOI":"10.1002\/rse2.200"},{"key":"1781_CR59","doi-asserted-by":"publisher","unstructured":"Kocabas, M., Athanasiou, N., & Black, M. J. (2020). VIBE: Video inference for human body pose and shape estimation. In Proceedings IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5252\u20135262). IEEE. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00530","DOI":"10.1109\/CVPR42600.2020.00530"},{"key":"1781_CR60","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Huang, C.-H. P., Hilliges, O., & Black, M. J. (2021). PARE: Part attention regressor for 3D human body estimation. In Proceedings of international conference on computer vision (ICCV) (pp. 11127\u201311137).","DOI":"10.1109\/ICCV48922.2021.01094"},{"key":"1781_CR61","unstructured":"Kosaraju, V., Sadeghian, A., Mart\u00edn-Mart\u00edn, R., Reid, I., Rezatofighi, H., & Savarese, S. (2019). Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. In Advances in neural information processing systems"},{"key":"1781_CR62","doi-asserted-by":"crossref","unstructured":"Lam, W., Liu, L., Prasad, S., Rajaraman, A., Vacheri, Z., & Doan, A. (2012). Muppet: Mapreduce-style processing of fast data. arXiv preprint arXiv:1208.4175","DOI":"10.14778\/2367502.2367520"},{"key":"1781_CR63","unstructured":"Lamb, A. M., Goyal, A. G. A. P., Zhang, Y., Zhang, S., Courville, A. C., & Bengio, Y. (2016). Professor forcing: A new algorithm for training recurrent networks. In Advances in neural information processing systems (pp. 4601\u20134609)."},{"key":"1781_CR64","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017). Temporal convolutional networks for action segmentation and detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 156\u2013165).","DOI":"10.1109\/CVPR.2017.113"},{"key":"1781_CR65","doi-asserted-by":"crossref","unstructured":"Lea, C., Reiter, A., Vidal, R., & Hager, G.D. (2016) Segmental spatiotemporal cnns for fine-grained action segmentation. In ECCV.","DOI":"10.1007\/978-3-319-46487-9_3"},{"key":"1781_CR66","doi-asserted-by":"crossref","unstructured":"Lea, C., Reiter, A., Vidal, R., & Hager, G. D. (2016). Segmental spatiotemporal cnns for fine-grained action segmentation. In European conference on computer vision (pp. 36\u201352). Springer","DOI":"10.1007\/978-3-319-46487-9_3"},{"key":"1781_CR67","doi-asserted-by":"crossref","unstructured":"Lee, N., Choi, W., Vernaza, P., Choy, C. B., Torr, P. H., & Chandraker, M. (2017). Desire: Distant future prediction in dynamic scenes with interacting agents. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 336\u2013345).","DOI":"10.1109\/CVPR.2017.233"},{"key":"1781_CR68","doi-asserted-by":"crossref","unstructured":"Li, S., Gunel, S., Ostrek, M., Ramdya, P., Fua, P., & Rhodin, H. (2020). Deformation-aware unpaired image translation for pose estimation on laboratory animals. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13158\u201313168).","DOI":"10.1109\/CVPR42600.2020.01317"},{"key":"1781_CR69","doi-asserted-by":"crossref","unstructured":"Li, S., Li, J., Tang, H., Qian, R., & Lin, W. (2019). Atrw: A benchmark for amur tiger re-identification in the wild. arXiv preprint arXiv:1906.05586","DOI":"10.1145\/3394171.3413569"},{"key":"1781_CR70","doi-asserted-by":"crossref","unstructured":"Li, Y., Wu, C.-Y., Fan, H., Mangalam, K., Xiong, B., Malik, J., & Feichtenhofer, C. (2021). Improved multiscale vision transformers for classification and detection. arXiv preprint arXiv:2112.01526","DOI":"10.1109\/CVPR52688.2022.00476"},{"key":"1781_CR71","doi-asserted-by":"crossref","unstructured":"Liang, J., Jiang, L., Niebles, J. C., Hauptmann, A. G., & Fei-Fei, L. (2019). Peeking into the future: Predicting future person activities and locations in videos. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5725\u20135734).","DOI":"10.1109\/CVPRW.2019.00358"},{"key":"1781_CR72","doi-asserted-by":"crossref","unstructured":"Lin, Y., Guo, X., & Lu, Y. (2021). Self-supervised video representation learning with meta-contrastive network. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 8239\u20138249).","DOI":"10.1109\/ICCV48922.2021.00813"},{"key":"1781_CR73","doi-asserted-by":"crossref","unstructured":"Lin, T., Liu, X., Li, X., Ding, E., & Wen, S. (2019). Bmn: Boundary-matching network for temporal action proposal generation. In Proceedings of the IEEE\/CVF international conference on computer vision","DOI":"10.1109\/ICCV.2019.00399"},{"key":"1781_CR74","doi-asserted-by":"crossref","unstructured":"Lin, T., Zhao, X., Su, H., Wang, C., & Yang, M. (2018). Bsn: Boundary sensitive network for temporal action proposal generation. In European conference on computer vision.","DOI":"10.1007\/978-3-030-01225-0_1"},{"key":"1781_CR75","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al. (2021). Swin transformer v2: Scaling up capacity and resolution. arXiv preprint arXiv:2111.09883","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"1781_CR76","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"1781_CR77","unstructured":"Lomonaco, V., & Maltoni, D. (2017). Core50: A new dataset and benchmark for continuous object recognition. In Conference on robot learning (pp. 17\u201326). PMLR"},{"key":"1781_CR78","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.jneumeth.2017.05.006","volume":"300","author":"M Lorbach","year":"2018","unstructured":"Lorbach, M., Kyriakou, E. I., Poppe, R., van Dam, E. A., Noldus, L. P. J. J., & Veltkamp, R. C. (2018). Learning to recognize rat social behavior: Novel dataset and cross-dataset application. Journal of Neuroscience Methods, 300, 166\u2013172.","journal-title":"Journal of Neuroscience Methods"},{"issue":"1","key":"1781_CR79","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1111\/tops.12455","volume":"12","author":"LC Loschky","year":"2020","unstructured":"Loschky, L. C., Larson, A. M., Smith, T. J., & Magliano, J. P. (2020). The scene perception and event comprehension theory (spect) applied to visual narratives. Topics in Cognitive Science, 12(1), 311\u2013351.","journal-title":"Topics in Cognitive Science"},{"key":"1781_CR80","unstructured":"Lotter, W., Kreiman, G., & Cox, D. (2017). Deep predictive coding networks for video prediction and unsupervised learning. In International conference on learning representations."},{"key":"1781_CR81","doi-asserted-by":"crossref","unstructured":"Luong, T., Pham, H., & Manning, C. D. (2015). Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 conference on empirical methods in natural language processing.","DOI":"10.18653\/v1\/D15-1166"},{"key":"1781_CR82","doi-asserted-by":"crossref","unstructured":"Malmaud, J., Huang, J., Rathod, V., Johnston, N., Rabinovich, A., & Murphy, K. (2015). What\u2019s cookin\u2019? interpreting cooking videos using text, speech and vision. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.","DOI":"10.3115\/v1\/N15-1015"},{"key":"1781_CR83","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.conb.2019.10.008","volume":"60","author":"MW Mathis","year":"2020","unstructured":"Mathis, M. W., & Mathis, A. (2020). Deep learning tools for the measurement of animal behavior in neuroscience. Current Opinion in Neurobiology, 60, 1\u201311.","journal-title":"Current Opinion in Neurobiology"},{"key":"1781_CR84","unstructured":"McIntosh, D., Marques, T. P., Albu, A. B., Rountree, R., & De\u00a0Leo, F. (2020). Movement tracks for the automatic detection of fish behavior in videos. arXiv preprint arXiv:2011.14070"},{"key":"1781_CR85","unstructured":"Metcalf, K., & Leake, D. (2017). Modelling unsupervised event segmentation: Learning event boundaries from prediction errors. In CogSci."},{"key":"1781_CR86","doi-asserted-by":"crossref","unstructured":"Mittal, S., Galesso, S., & Brox, T. (2021) Essentials for class incremental learning. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops (pp. 3513\u20133522).","DOI":"10.1109\/CVPRW53098.2021.00390"},{"key":"1781_CR87","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., & Claudel, C. (2020). Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 14424\u201314432).","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"1781_CR88","unstructured":"NOAA Arctic Seals. https:\/\/lila.science\/datasets\/arcticseals"},{"key":"1781_CR89","unstructured":"Noninvasive bee tracking in videos: Deep learning algorithms and cloud platform design specifications (2021)"},{"key":"1781_CR90","doi-asserted-by":"crossref","unstructured":"Pan, T., Song, Y., Yang, T., Jiang, W., & Liu, W. (2021). Videomoco: Contrastive video representation learning with temporally adversarial examples. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11205\u201311214).","DOI":"10.1109\/CVPR46437.2021.01105"},{"key":"1781_CR91","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Haurum, J. B., Bengtson, S. H., & Moeslund, T. B. (2020). 3D-zef: A 3D zebrafish tracking benchmark dataset. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2426\u20132436).","DOI":"10.1109\/CVPR42600.2020.00250"},{"key":"1781_CR92","doi-asserted-by":"crossref","unstructured":"Petrovich, M., Black, M. J., & Varol, G. (2021). Action-conditioned 3D human motion synthesis with transformer VAE. In Proceedings of international conference on computer vision (ICCV) (pp. 10985\u201310995).","DOI":"10.1109\/ICCV48922.2021.01080"},{"key":"1781_CR93","doi-asserted-by":"crossref","unstructured":"Price, W., Vondrick, C., & Damen, D. (2022) Unweavenet: Unweaving activity stories. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13770\u201313779).","DOI":"10.1109\/CVPR52688.2022.01340"},{"key":"1781_CR94","unstructured":"Qiu, J., Huang, G., & Lee, T. S. (2019). A neurally-inspired hierarchical prediction network for spatiotemporal sequence learning and prediction. arXiv preprint arXiv:1901.09002"},{"key":"1781_CR95","unstructured":"Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training"},{"issue":"8","key":"1781_CR96","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.","journal-title":"OpenAI Blog"},{"issue":"8","key":"1781_CR97","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1080\/17470218.2011.571267","volume":"64","author":"GA Radvansky","year":"2011","unstructured":"Radvansky, G. A., Krawietz, S. A., & Tamplin, A. K. (2011). Walking through doorways causes forgetting: Further explorations. Quarterly Journal of Experimental Psychology, 64(8), 1632\u20131645.","journal-title":"Quarterly Journal of Experimental Psychology"},{"key":"1781_CR98","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199898138.001.0001","volume-title":"Event cognition","author":"GA Radvansky","year":"2014","unstructured":"Radvansky, G. A., & Zacks, J. M. (2014). Event cognition. Oxford University Press."},{"issue":"2","key":"1781_CR99","doi-asserted-by":"publisher","first-page":"0239504","DOI":"10.1371\/journal.pone.0239504","volume":"16","author":"MN Ratnayake","year":"2021","unstructured":"Ratnayake, M. N., Dyer, A. G., & Dorin, A. (2021). Tracking individual honeybees among wildflower clusters with computer vision-facilitated pollinator monitoring. PLoS ONE, 16(2), 0239504.","journal-title":"PLoS ONE"},{"key":"1781_CR100","doi-asserted-by":"crossref","unstructured":"Richard, A., Kuehne, H., & Gall, J. (2017) Weakly supervised action learning with rnn based fine-to-coarse modeling. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 754\u2013763).","DOI":"10.1109\/CVPR.2017.140"},{"key":"1781_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2020.101076","volume":"41","author":"DS Rodriguez-Baena","year":"2020","unstructured":"Rodriguez-Baena, D. S., Gomez-Vela, F. A., Garc\u00eda-Torres, M., Divina, F., Barranco, C. D., Daz-Diaz, N., Jimenez, M., & Montalvo, G. (2020). Identifying livestock behavior patterns based on accelerometer dataset. Journal of Computational Science, 41, 101076. https:\/\/doi.org\/10.1016\/j.jocs.2020.101076","journal-title":"Journal of Computational Science"},{"issue":"3","key":"1781_CR102","doi-asserted-by":"publisher","first-page":"421","DOI":"10.3390\/jzbg2030031","volume":"2","author":"PE Rose","year":"2021","unstructured":"Rose, P. E., & Riley, L. M. (2021). Conducting behavioural research in the zoo: A guide to ten important methods, concepts and theories. Journal of Zoological and Botanical Gardens, 2(3), 421\u2013444. https:\/\/doi.org\/10.3390\/jzbg2030031","journal-title":"Journal of Zoological and Botanical Gardens"},{"key":"1781_CR103","doi-asserted-by":"crossref","unstructured":"Sahoo, D., Pham, Q., Lu, J., & Hoi, S. C. H. (2018). Online deep learning: Learning deep neural networks on the fly. In Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18.","DOI":"10.24963\/ijcai.2018\/369"},{"key":"1781_CR104","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., & Pavone, M. (2020) Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVIII 16 (pp. 683\u2013700). Springer","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"1781_CR105","doi-asserted-by":"crossref","unstructured":"Sanakoyeu, A., Khalidov, V., McCarthy, M. S., Vedaldi, A., & Neverova, N. (2020). Transferring dense pose to proximal animal classes. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5233\u20135242).","DOI":"10.1109\/CVPR42600.2020.00528"},{"issue":"170","key":"1781_CR106","doi-asserted-by":"publisher","first-page":"20200179","DOI":"10.1098\/rsif.2020.0179","volume":"17","author":"R Sarfati","year":"2020","unstructured":"Sarfati, R., Hayes, J. C., Sarfati, \u00c9., & Peleg, O. (2020). Spatio-temporal reconstruction of emergent flash synchronization in firefly swarms via stereoscopic 360-degree cameras. Journal of The Royal Society Interface, 17(170), 20200179.","journal-title":"Journal of The Royal Society Interface"},{"key":"1781_CR107","unstructured":"Scenedetect: Video Scene Cut Detection and Analysis Tool. https:\/\/github.com\/Breakthrough\/PySceneDetect"},{"key":"1781_CR108","doi-asserted-by":"crossref","unstructured":"Sener, F., & Yao, A. (2018). Unsupervised learning and segmentation of complex activities from video. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8368\u20138376).","DOI":"10.1109\/CVPR.2018.00873"},{"key":"1781_CR109","doi-asserted-by":"crossref","unstructured":"Shao, D., Zhao, Y., Dai, B., & Lin, D. (2020). Intra- and inter-action understanding via temporal action parsing. In IEEE Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00081"},{"key":"1781_CR110","doi-asserted-by":"crossref","unstructured":"Shou, M.Z., Lei, S.W., Wang, W., Ghadiyaram, D., & Feiszli, M. (2021). Generic event boundary detection: A benchmark for event segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV48922.2021.00797"},{"key":"1781_CR111","doi-asserted-by":"crossref","unstructured":"Silvia\u00a0Zuffi, T. B.-W. M. J. B., & Angjoo, K. (2019). Three-d safari: Learning to estimate zebra pose, shape, and texture from images \u201cin the wild\u201d. In The IEEE international conference on computer vision (ICCV).","DOI":"10.1109\/ICCV.2019.00546"},{"issue":"4","key":"1781_CR112","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3758\/CABN.3.4.335","volume":"3","author":"NK Speer","year":"2003","unstructured":"Speer, N. K., Swallow, K. M., & Zacks, J. M. (2003). Activation of human motion processing areas during event perception. Cognitive, Affective, and Behavioral Neuroscience, 3(4), 335\u2013345.","journal-title":"Cognitive, Affective, and Behavioral Neuroscience"},{"key":"1781_CR113","unstructured":"Sun, J. J., Karigo, T., Chakraborty, D., Mohanty, S. P., Wild, B., Sun, Q., Chen, C., Anderson, D. J., Perona, P., Yue, Y., & Kennedy, A. (2021). The multi-agent behavior dataset: Mouse dyadic social interactions. arXiv preprint arXiv:2104.02710"},{"key":"1781_CR114","doi-asserted-by":"crossref","unstructured":"Sun, W., Zhang, J., Wang, D., Geng, Y.-a., & Li, Q. (2021). Ilcoc: An incremental learning framework based on contrastive one-class classifiers. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops (pp. 3580\u20133588).","DOI":"10.1109\/CVPRW53098.2021.00397"},{"issue":"1","key":"1781_CR115","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2015.26","volume":"2","author":"A Swanson","year":"2015","unstructured":"Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015). Snapshot serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Scientific Data, 2(1), 1\u201314.","journal-title":"Scientific Data"},{"key":"1781_CR116","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818\u20132826).","DOI":"10.1109\/CVPR.2016.308"},{"key":"1781_CR117","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.conb.2021.01.005","volume":"68","author":"C Testard","year":"2021","unstructured":"Testard, C., Tremblay, S., & Platt, M. (2021). From the field to the lab and back: Neuroethology of primate social behavior. Current Opinion in Neurobiology, 68, 76\u201383.","journal-title":"Current Opinion in Neurobiology"},{"issue":"1","key":"1781_CR118","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-27980-y","volume":"13","author":"D Tuia","year":"2022","unstructured":"Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications, 13(1), 1\u201315.","journal-title":"Nature Communications"},{"key":"1781_CR119","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.anbehav.2016.12.005","volume":"124","author":"JJ Valletta","year":"2017","unstructured":"Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017). Applications of machine learning in animal behaviour studies. Animal Behaviour, 124, 203\u2013220.","journal-title":"Animal Behaviour"},{"key":"1781_CR120","doi-asserted-by":"crossref","unstructured":"Vasconcelos, C., Birodkar, V., & Dumoulin, V. (2022). Proper reuse of image classification features improves object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13628\u201313637).","DOI":"10.1109\/CVPR52688.2022.01326"},{"key":"1781_CR121","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998\u20136008)."},{"key":"1781_CR122","doi-asserted-by":"crossref","unstructured":"VidalMata, R. G., Scheirer, W. J., Kukleva, A., Cox, D., & Kuehne, H. (2021). Joint visual-temporal embedding for unsupervised learning of actions in untrimmed sequences. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision (WACV) (pp. 1238\u20131247).","DOI":"10.1109\/WACV48630.2021.00128"},{"key":"1781_CR123","doi-asserted-by":"crossref","unstructured":"Wang, C., Cai, S., & Tan, G. (2021). Graphtcn: Spatio-temporal interaction modeling for human trajectory prediction. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision (pp. 3450\u20133459).","DOI":"10.1109\/WACV48630.2021.00349"},{"key":"1781_CR124","unstructured":"Wang, Y., Gao, Z., Long, M., Wang, J., & Yu, P. S. (2018). Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. arXiv preprint arXiv:1804.06300"},{"key":"1781_CR125","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ecoinf.2018.12.002","volume":"49","author":"G Wang","year":"2019","unstructured":"Wang, G. (2019). Machine learning for inferring animal behavior from location and movement data. Ecological Informatics, 49, 69\u201376.","journal-title":"Ecological Informatics"},{"key":"1781_CR126","unstructured":"Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., & Perona, P. (2010). Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology"},{"key":"1781_CR127","doi-asserted-by":"crossref","unstructured":"Weyand, T., Araujo, A., Cao, B., & Sim, J. (2020). Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2575\u20132584).","DOI":"10.1109\/CVPR42600.2020.00265"},{"key":"1781_CR128","unstructured":"wichers, N., Villegas, R., Erhan, D., & Lee, H. (2018). Hierarchical long-term video prediction without supervision. In Proceedings of the 35th international conference on machine learning."},{"issue":"9","key":"1781_CR129","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2019","unstructured":"Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2019). Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(9), 2251\u20132265. https:\/\/doi.org\/10.1109\/TPAMI.2018.2857768","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1781_CR130","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., & Bengio, Y. (2015). Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning (pp. 2048\u20132057)."},{"key":"1781_CR131","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, X., Arnab, A., Lu, Z., Zhang, M., Sun, C., & Schmid, C. (2022). Multiview transformers for video recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 3333\u20133343).","DOI":"10.1109\/CVPR52688.2022.00333"},{"key":"1781_CR132","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., & Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in neural information processing systems (pp. 5754\u20135764)."},{"key":"1781_CR133","doi-asserted-by":"crossref","unstructured":"Yao, Y., Mohan, A., Bliss-Moreau, E., Coleman, K., Freeman, S. M., Machado, C. J., Raper, J., Zimmermann, J., Hayden, B. Y., & Park, H. S. (2021). Openmonkeychallenge: Dataset and benchmark challenges for pose tracking of non-human primates. bioRxiv","DOI":"10.1101\/2021.09.08.459549"},{"issue":"6","key":"1781_CR134","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1207\/s15516709cog2806_5","volume":"28","author":"JM Zacks","year":"2004","unstructured":"Zacks, J. M. (2004). Using movement and intentions to understand simple events. Cognitive Science, 28(6), 979\u20131008.","journal-title":"Cognitive Science"},{"issue":"2","key":"1781_CR135","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1037\/0033-2909.133.2.273","volume":"133","author":"JM Zacks","year":"2007","unstructured":"Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007). Event perception: A mind-brain perspective. Psychological Bulletin, 133(2), 273.","journal-title":"Psychological Bulletin"},{"issue":"2","key":"1781_CR136","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1111\/j.1467-8721.2007.00480.x","volume":"16","author":"JM Zacks","year":"2007","unstructured":"Zacks, J. M., & Swallow, K. M. (2007). Event segmentation. Current Directions in Psychological Science, 16(2), 80\u201384.","journal-title":"Current Directions in Psychological Science"},{"issue":"1","key":"1781_CR137","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1037\/0033-2909.127.1.3","volume":"127","author":"JM Zacks","year":"2001","unstructured":"Zacks, J. M., & Tversky, B. (2001). Event structure in perception and conception. Psychological Bulletin, 127(1), 3.","journal-title":"Psychological Bulletin"},{"issue":"1","key":"1781_CR138","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1037\/0096-3445.130.1.29","volume":"130","author":"JM Zacks","year":"2001","unstructured":"Zacks, J. M., Tversky, B., & Iyer, G. (2001). Perceiving, remembering, and communicating structure in events. Journal of Experimental Psychology: General, 130(1), 29.","journal-title":"Journal of Experimental Psychology: General"},{"key":"1781_CR139","unstructured":"Zaheer, M., Guruganesh, G., Dubey, K.A., Ainslie, J., Alberti, C., Onta\u00f1\u00f3n, S., Pham, P., Ravula, A., Wang, Q., Yang, L., & Ahmed, A. (2020). Big bird: Transformers for longer sequences. In Advances in neural information processing systems (vol. 33) (NeurIPS)"},{"key":"1781_CR140","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., & Deny, S. (2021). Barlow twins: Self-supervised learning via redundancy reduction. In International conference on machine learning (pp. 12310\u201312320). PMLR"},{"key":"1781_CR141","unstructured":"Zhang, L., Dunn, T., Marshall, J., Olveczky, B., & Linderman, S. (2021). Animal pose estimation from video data with a hierarchical von mises-fisher-gaussian model. In International conference on artificial intelligence and statistics (pp. 2800\u20132808). PMLR"},{"key":"1781_CR142","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, X., Liu, C., Shuai, B., Zhu, Y., Brattoli, B., Chen, H., Marsic, I., & Tighe, J. (2021). Vidtr: Video transformer without convolutions. In Proceedings of the IEEE\/CVF international conference on computer vision.","DOI":"10.1109\/ICCV48922.2021.01332"},{"key":"1781_CR143","unstructured":"Zhou, J., Wei, C., Wang, H., Shen, W., Xie, C., Yuille, A., & Kong, T. (2021). ibot: Image bert pre-training with online tokenizer. In International conference on learning representations."},{"key":"1781_CR144","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1109\/TIP.2020.3039886","volume":"30","author":"W Zhu","year":"2020","unstructured":"Zhu, W., Lu, J., Li, J., & Zhou, J. (2020). Dsnet: A flexible detect-to-summarize network for video summarization. IEEE Transactions on Image Processing, 30, 948\u2013962.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"7","key":"1781_CR145","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","volume":"27","author":"Z Zivkovic","year":"2006","unstructured":"Zivkovic, Z., & Van Der Heijden, F. (2006). Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters, 27(7), 773\u2013780.","journal-title":"Pattern Recognition Letters"},{"key":"1781_CR146","doi-asserted-by":"crossref","unstructured":"Zuffi, S., Kanazawa, A., & Black, M.J. (2018). Lions and tigers and bears: Capturing non-rigid, 3D, articulated shape from images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3955\u20133963).","DOI":"10.1109\/CVPR.2018.00416"},{"key":"1781_CR147","doi-asserted-by":"crossref","unstructured":"Zuffi, S., Kanazawa, A., Jacobs, D., & Black, M.J. (2017). 3D menagerie: Modeling the 3D shape and pose of animals. In IEEE Conferences\u00a0on computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2017.586"}],"updated-by":[{"DOI":"10.1007\/s11263-023-01847-1","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000}}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01781-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01781-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01781-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T03:24:47Z","timestamp":1690601087000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01781-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":147,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1781"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01781-2","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s11263-023-01847-1","asserted-by":"object"}]},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]},"assertion":[{"value":"29 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11263-023-01847-1","URL":"https:\/\/doi.org\/10.1007\/s11263-023-01847-1","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}}]}}