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We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations.
Isolation of a putative phospholipase c gene of drosophila, norpa, and its role in phototransduction
Bloomquist, B. T., Shortridge, R., Schneuwly, S., Perdew, M., Montell, C., Steller, H., Rubin, G., and Pak, W · 1988
Earlier work this paper cites.
Drosophila: genetics meets behaviour
Sokolowski, M. B · 2001
Earlier work this paper cites.
Gender-selective patterns of aggressive behavior in drosophila melanogaster
Nilsen, S. P., Chan, Y.-B., Huber, R., and Kravitz, E. A · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Dalal, N. and Triggs, B · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T · 2011
Earlier work this paper cites.
Social behavior recognition in continuous video
Burgos-Artizzu, X. P., Dollár, P., Lin, D., Anderson, D. J., and Perona, P · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., and Shah, M · 2012
Earlier work this paper cites.
Jaaba: interactive machine learning for automatic annotation of animal behavior
Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S., and Branson, K · 2013
Earlier work this paper cites.
Toward a science of computational ethology
Anderson, D. J. and Perona, P · 2014
Earlier work this paper cites.
Mushroom body output neurons encode valence and guide memory-based action selection in drosophila
Aso, Y., Sitaraman, D., Ichinose, T., Kaun, K. R., Vogt, K., Belliart-Guérin, G., Plaçais, P.-Y., Robie, A. A., Yamagata, N., Schnaitmann, C., et al · 2014
Earlier work this paper cites.
Mapping the stereotyped behaviour of freely moving fruit flies
Berman, G. J., Choi, D. M., Bialek, W., and Shaevitz, J. W · 2014
Earlier work this paper cites.
Detecting social actions of fruit flies
Eyjolfsdottir, E., Branson, S., Burgos-Artizzu, X. P., Hoopfer, E. D., Schor, J., Anderson, D. J., and Perona, P · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Independent optical excitation of distinct neural populations
Klapoetke, N. C., Murata, Y., Kim, S. S., Pulver, S. R., Birdsey-Benson, A., Cho, Y. K., Morimoto, T. K., Chuong, A. S., Carpenter, E. J., Tian, Z., et al · 2014
Earlier work this paper cites.
Learning fine-grained spatial models for dynamic sports play prediction
Yue, Y., Lucey, P., Carr, P., Bialkowski, A., and Matthews, I · 2014
Earlier work this paper cites.
Learning to see by moving
Agrawal, P., Carreira, J., and Malik, J · 2015
Earlier work this paper cites.
Unsupervised learning of spatiotemporally coherent metrics
Goroshin, R., Bruna, J., Tompson, J., Eigen, D., and LeCun, Y · 2015
Earlier work this paper cites.
Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning
Hong, W., Kennedy, A., Burgos-Artizzu, X. P., Zelikowsky, M., Navonne, S. G., Perona, P., and Anderson, D. J · 2015
Earlier work this paper cites.
Mapping sub-second structure in mouse behavior
Wiltschko, A. B., Johnson, M. J., Iurilli, G., Peterson, R. E., Katon, J. M., Pashkovski, S. L., Abraira, V. E., Adams, R. P., and Datta, S. R · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Shuffle and learn: unsupervised learning using temporal order verification
Misra, I., Zitnick, C. L., and Hebert, M · 2016
Earlier work this paper cites.
An uncertain future: Forecasting from static images using variational autoencoders
Walker, J., Doersch, C., Gupta, A., and Hebert, M · 2016
Earlier work this paper cites.
Visual projection neurons in the drosophila lobula link feature detection to distinct behavioral programs
Wu, M., Nern, A., Williamson, W. R., Morimoto, M. M., Reiser, M. B., Card, G. M., and Rubin, G. M · 2016
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Earlier work this paper cites.
The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al · 2017
Earlier work this paper cites.
An unsupervised method for quantifying the behavior of paired animals
Klibaite, U., Berman, G. J., Cande, J., Stern, D. L., and Shaevitz, J. W · 2017
Earlier work this paper cites.
Associative embedding: End-to-end learning for joint detection and grouping
Newell, A., Huang, Z., and Deng, J · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
Cited alongside, same era.
Mapping the neural substrates of behavior
Robie, A. A., Hirokawa, J., Edwards, A. W., Umayam, L. A., Lee, A., Phillips, M. L., Card, G. M., Korff, W., Rubin, G. M., Simpson, J. H., et al · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Co-Reyes, J. D., Liu, Y., Gupta, A., Eysenbach, B., Abbeel, P., and Levine, S · 2018
Cited alongside, same era.
Automatic discovery of tactics in spatio-temporal soccer match data
Decroos, T., Van Haaren, J., and Davis, J · 2018
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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A framework for studying behavioral evolution by reconstructing ancestral repertoires
Hernández, D. G., Rivera, C., Cande, J., Zhou, B., Stern, D. L., and Berman, G. J · 2020
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B-soid: An open source unsupervised algorithm for discovery of spontaneous behaviors
Hsu, A. I. and Yttri, E. A · 2020
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SpanBERT: Improving pre-training by representing and predicting spans
Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., and Levy, O · 2020
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Identifying behavioral structure from deep variational embeddings of animal motion
Luxem, K., Fuhrmann, F., Kürsch, J., Remy, S., and Bauer, P · 2020
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Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
The striatum organizes 3d behavior via moment-to-moment action selection
Markowitz, J. E., Gillis, W. F., Beron, C. C., Neufeld, S. Q., Robertson, K., Bhagat, N. D., Peterson, R. E., Peterson, E., Hyun, M., Linderman, S. W., et al · 2018
Cited alongside, same era.
Structure of the zebrafish locomotor repertoire revealed with unsupervised behavioral clustering
Marques, J. C., Lackner, S., Félix, R., and Orger, M. B · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Quantifying behavior to understand the brain
Pereira, T. D., Shaevitz, J. W., and Murthy, M · 2020
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Cell types and neuronal circuitry underlying female aggression in drosophila
Schretter, C. E., Aso, Y., Robie, A. A., Dreher, M., Dolan, M.-J., Chen, N., Ito, M., Yang, T., Parekh, R., Branson, K. M., et al · 2020
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Integrative benchmarking to advance neurally mechanistic models of human intelligence
Schrimpf, M., Kubilius, J., Lee, M. J., Murty, N. A. R., Ajemian, R., and DiCarlo, J. J · 2020
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The mouse action recognition system (mars): a software pipeline for automated analysis of social behaviors in mice
Segalin, C., Williams, J., Karigo, T., Hui, M., Zelikowsky, M., Sun, J. J., Perona, P., Anderson, D. J., and Kennedy, A · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al · 2020
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Revealing the structure of pharmacobehavioral space through motion sequencing
Wiltschko, A. B., Tsukahara, T., Zeine, A., Anyoha, R., Gillis, W. F., Markowitz, J. E., Peterson, R. E., Katon, J., Johnson, M. J., and Datta, S. R · 2020
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Learning calibratable policies using programmatic style-consistency
Zhan, E., Tseng, A., Yue, Y., Swaminathan, A., and Hausknecht, M · 2020
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Beit: Bert pre-training of image transformers
Bao, H., Dong, L., and Wei, F · 2021
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Deepethogram, a machine learning pipeline for supervised behavior classification from raw pixels
Bohnslav, J. P., Wimalasena, N. K., Clausing, K. J., Dai, Y. Y., Yarmolinsky, D. A., Cruz, T., Kashlan, A. D., Chiappe, M. E., Orefice, L. L., Woolf, C. J., et al · 2021
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A large-scale study on unsupervised spatiotemporal representation learning
Feichtenhofer, C., Fan, H., Xiong, B., Girshick, R., and He, K · 2021
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Perceiver io: A general architecture for structured inputs & outputs
Jaegle, A., Borgeaud, S., Alayrac, J.-B., Doersch, C., Ionescu, C., Ding, D., Koppula, S., Zoran, D., Brock, A., Shelhamer, E., et al · 2021
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Benchmarking representation learning for natural world image collections
Van Horn, G., Cole, E., Beery, S., Wilber, K., Belongie, S., and Mac Aodha, O · 2021
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Unsupervised learning of neurosymbolic encoders
Zhan, E., Sun, J. J., Kennedy, A., Yue, Y., and Chaudhuri, S · 2021
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Video based phenotyping platform for the laboratory mouse
Beane, G., Geuther, B. Q., Sproule, T. J., Trapszo, J., Hession, L., Kohar, V., and Kumar, V · 2022
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Masked autoencoders as spatiotemporal learners
Feichtenhofer, C., Fan, H., Li, Y., and He, K · 2022
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Apt: Animal part tracker v0.3.4, March 2022
Kabra, M., Lee, A., Robie, A., Egnor, R., Huston, S., Rodriguez, I. F., Edwards, A., and Branson, K · 2022
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Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models
Klibaite, U., Kislin, M., Verpeut, J. L., Bergeler, S., Sun, X., Shaevitz, J. W., and Wang, S. S.-H · 2022
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Mvitv2: Improved multiscale vision transformers for classification and detection
Li, Y., Wu, C.-Y., Fan, H., Mangalam, K., Xiong, B., Malik, J., and Feichtenhofer, C · 2022
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Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments
Marks, M., Jin, Q., Sturman, O., von Ziegler, L., Kollmorgen, S., von der Behrens, W., Mante, V., Bohacek, J., and Yanik, M. F · 2022
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Animal kingdom: A large and diverse dataset for animal behavior understanding
Ng, X. L., Ong, K. E., Zheng, Q., Ni, Y., Yeo, S. Y., and Liu, J · 2022
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Stride-level analysis of mouse open field behavior using deep-learning-based pose estimation
Sheppard, K., Gardin, J., Sabnis, G., Peer, A., Darrell, M., Deats, S., Geuther, B., Lutz, C. M., and Kumar, V · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Tong, Z., Song, Y., Wang, J., and Wang, L · 2022
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Masked feature prediction for self-supervised visual pre-training
Wei, C., Fan, H., Xie, S., Wu, C.-Y., Yuille, A., and Feichtenhofer, C · 2022
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