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This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence.
Object permanence in five-month-old infants
Baillargeon, R., Spelke, E. S., and Wasserman, S · 1985
Earlier work this paper cites.
Principles of object perception
Spelke, E. S · 1990
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R · 1998
Earlier work this paper cites.
Tracking multiple objects through occlusions
Huang, Y. and Essa, I · 2005
Earlier work this paper cites.
Multiple target tracking using spatio-temporal markov chain monte carlo data association
Yu, Q., Medioni, G., and Cohen, I · 2007
Earlier work this paper cites.
Evaluating multiple object tracking performance: the clear mot metrics
Bernardin, K. and Stiefelhagen, R · 2008
Earlier work this paper cites.
Robust tracking-by-detection using a detector confidence particle filter
Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L · 2009
Earlier work this paper cites.
Learning to associate: Hybridboosted multi-target tracker for crowded scene
Li, Y., Huang, C., and Nevatia, R · 2009
Earlier work this paper cites.
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
Earlier work this paper cites.
Tracking the invisible: Learning where the object might be
Grabner, H., Matas, J., Van Gool, L., and Cattin, P · 2010
Earlier work this paper cites.
Multi-person tracking with sparse detection and continuous segmentation
Mitzel, D., Horbert, E., Ess, A., and Leibe, B · 2010
Earlier work this paper cites.
Multiple objects tracking in the presence of long-term occlusions
Papadourakis, V. and Argyros, A · 2010
Earlier work this paper cites.
Blender Game Engine: Beginner’s Guide
Bacone, V. K · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R · 2012
Earlier work this paper cites.
DART: Dense articulated real-time tracking
Schmidt, T., Newcombe, R. A., and Fox, D · 2014
Earlier work this paper cites.
Near-online multi-target tracking with aggregated local flow descriptor
Choi, W · 2015
Earlier work this paper cites.
FlowNet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., and Brox, T · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Delving deeper into convolutional networks for learning video representations
Ballas, N., Yao, L., Pal, C., and Courville, A · 2016
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Simple online and realtime tracking
Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B · 2016
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Learning dense correspondence via 3D-guided cycle consistency
Zhou, T., Krahenbuhl, P., Aubry, M., Huang, Q., and Efros, A. A · 2016
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Detect to track and track to detect
Feichtenhofer, C., Pinz, A., and Zisserman, A · 2017
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Mask R-CNN
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
Cited alongside, same era.
Classifying, segmenting, and tracking object instances in video with mask propagation
Bertasius, G. and Torresani, L · 2020
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nuScenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2020
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TAO: A large-scale benchmark for tracking any object
Dave, A., Khurana, T., Tokmakov, P., Schmid, C., and Ramanan, D · 2020
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Semantics for robotic mapping, perception and interaction: A survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I., Gould, S., et al · 2020
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CATER: A diagnostic dataset for compositional actions and temporal reasoning
Girdhar, R. and Ramanan, D · 2020
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SMAT: Smart multiple affinity metrics for multiple object tracking
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Object detection in videos with tubelet proposal networks
Kang, K., Li, H., Xiao, T., Ouyang, W., Yan, J., Liu, X., and Wang, X · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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The 2017 DAVIS challenge on video object segmentation
Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., and Van Gool, L · 2017
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Multiple people tracking by lifted multicut and person re-identification
Tang, S., Andriluka, M., Andres, B., and Schiele, B · 2017
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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.
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., and Paulus, D · 2017
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Gonzalez, N. F., Ospina, A., and Calvez, P · 2020
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Space-time correspondence as a contrastive random walk
Jabri, A., Owens, A., and Efros, A. A · 2020
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HOTA: A higher order metric for evaluating multi-object tracking
Luiten, J., Osep, A., Dendorfer, P., Torr, P., Geiger, A., Leal-Taixé, L., and Leibe, B · 2020
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Multiple object tracking: A literature review
Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., and Kim, T.-K · 2020
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Learning local feature descriptors for multiple object tracking
Mykheievskyi, D., Borysenko, D., and Porokhonskyy, V · 2020
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Learning object permanence from video
Shamsian, A., Kleinfeld, O., Globerson, A., and Chechik, G · 2020
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3D multi-object tracking: A baseline and new evaluation metrics
Weng, X. and Kitani, K · 2020
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Tracking objects as points
Zhou, X., Koltun, V., and Krähenbühl, P · 2020
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https://paralleldomain.com/ , March 2021
Parallel domain · 2021
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Is space-time attention all you need for video understanding?
Bertasius, G., Wang, H., and Torresani, L · 2021
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DEFT: Detection embeddings for tracking
Chaabane, M., Zhang, P., Beveridge, R., and O’Hara, S · 2021
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Learning to track with object permanence
Tokmakov, P., Li, J., Burgard, W., and Gaidon, A · 2021
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End-to-end video instance segmentation with transformers
Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., and Xia, H · 2021
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