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We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.
Discovering objects and their location in images
J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman · 2005
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Using multiple segmentations to discover objects and their extent in image collections
B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman · 2006
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Unsupervised segmentation of objects using efficient learning
H. Arora, N. Loeff, D. A. Forsyth, and N. Ahuja · 2007
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Decomposition, discovery and detection of visual categories using topic models
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Unsupervised modeling of object categories using link analysis techniques
G. Kim, C. Faloutsos, and M. Hebert · 2008
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, Y. LeCun, et al · 2009
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Unsupervised object discovery: A comparison
T. Tuytelaars, C. H. Lampert, M. B. Blaschko, and W. Buntine · 2009
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The moped framework: Object recognition and pose estimation for manipulation
A. C. Romea, M. M. Torres, and S. Srinivasa · 2011
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On random weights and unsupervised feature learning
A. M. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Y. Ng · 2011
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Segmenting “simple” objects using rgb-d
A. K. Mishra, A. Shrivastava, and Y. Aloimonos · 2012
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Object discovery in 3d scenes via shape analysis
A. Karpathy, S. Miller, and L. Fei-Fei · 2013
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Unsupervised object discovery and segmentation in videos
S. Schulter, C. Leistner, P. Roth, and H. Bischof · 2013
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Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 2013
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Video object discovery and co-segmentation with extremely weak supervision
L. Wang, G. Hua, R. Sukthankar, J. Xue, and N. Zheng · 2014
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Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Deep learning for single-view instance recognition
D. Held, S. Thrun, and S. Savarese · 2015
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B. G. V. Kumar, G. Carneiro, and I. D. Reid · 2015
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Unsupervised object discovery and tracking in video collections
S. Kwak, M. Cho, I. Laptev, J. Ponce, and C. Schmid · 2015
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Learning deep representations for ground-to-aerial geolocalization
T. Lin, Y. Cui, S. Belongie, and J. Hays · 2015
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Local convolutional features with unsupervised training for image retrieval
M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronin, and C. Schmid · 2015
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Imu-based gait recognition using convolutional neural networks and multi-sensor fusion
O. Dehzangi, M. Taherisadr, and R. ChangalVala · 2017
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Superpoint: Self-supervised interest point detection and description
D. DeTone, T. Malisiewicz, and A. Rabinovich · 2017
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Unsupervised object segmentation in video by efficient selection of highly probable positive features
E. Haller and M. Leordeanu · 2017
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Fusionseg: Learning to combine motion and appearance for fully automatic segmentation of generic objects in videos
S. D. Jain, B. Xiong, and K. Grauman · 2017
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A. Radford, L. Metz, and S. Chintala · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Unsupervised learning of visual representations using videos
X. Wang and A. Gupta · 2015
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Adversarially learned inference
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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A self-supervised learning system for object detection using physics simulation and multi-view pose estimation
C. Mitash, K. E. Bekris, and A. Boularias · 2017
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Curiosity-driven exploration by self-supervised prediction
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Learning features by watching objects move
D. Pathak, R. Girshick, P. Dollár, T. Darrell, and B. Hariharan · 2017
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D. Ulyanov, A. Vedaldi, and V. S. Lempitsky · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
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Tracking persons-of-interest via unsupervised representation adaptation
S. Zhang, J. Huang, J. Lim, Y. Gong, J. Wang, N. Ahuja, and M. Yang · 2017
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Scaling egocentric vision: The epic-kitchens dataset
D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray · 2018
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Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
P. R. Florence, L. Manuelli, and R. Tedrake · 2018
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Object category learning and retrieval with weak supervision
S. Hickson, A. Angelova, I. A. Essa, and R. Sukthankar · 2018
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Self-supervisory signals for object discovery and detection
E. Pot, A. Toshev, and J. Kosecka · 2018
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Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine · 2018
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
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