Z. Abderrahmane, G. Ganesh, A. Crosnier, and A. Cherubini, Haptic Zero-Shot Learning: Recognition of objects never touched before, Robotics and Autonomous Systems, vol.105, pp.11-25, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01625945

Z. Abderrahmane, G. Ganesh, A. Crosnier, and A. Cherubini, Visuo-tactile recognition of daily-life objects never seen or touched before, IEEE Int. Conf. on Control Automation Robotics & Vision (ICARCV), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01869015

Z. Abderrahmane, G. Ganesh, A. Crosnier, and A. Cherubini, A deep learning framework for tactile recognition of known as well as novel objects, Transactions on Industrial Informatics (TII), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02012628

M. Acer, A. F. Yildiz, and F. H. Bazzaz, Development of a soft pzt based tactile sensor array for force localization, Information, Communication and Automation Technologies, pp.1-6, 2017.

P. K. Allen and K. S. Roberts, Haptic object recognition using a multifingered dextrous hand, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.342-347, 1989.

T. Asfour, K. Regenstein, P. Azad, J. Schroder, A. Bierbaum et al., Armar-iii: An integrated humanoid platform for sensory-motor control, Humanoid Robots, 2006 6th IEEE-RAS International Conference on, pp.169-175, 2006.

Y. Bekiroglu, D. Kragic, and V. Kyrki, Learning grasp stability based on tactile data and hmms, 19th International Symposium in Robot and Human Interactive Communication, pp.132-137, 2010.

S. Bell, P. Upchurch, N. Snavely, and K. Bala, Material recognition in the wild with the materials in context database (supplemental material), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015.

R. Bellman, Dynamic programming (dp)

P. J. Besl and N. D. Mckay, Method for registration of 3-d shapes, Sensor Fusion IV: Control Paradigms and Data Structures, vol.1611, pp.586-607, 1992.

I. Biederman, Recognition-by-components: a theory of human image understanding, Psychological Review, vol.94, p.115, 1987.

A. Bierbaum, T. Asfour, and R. Dillmann, Dynamic potential fields for dexterous tactile exploration, Human Centered Robot Systems, pp.23-31, 2009.

M. Buda, A. Maki, and M. A. Mazurowski, A systematic study of the class imbalance problem in convolutional neural networks, 2017.

B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel et al., Benchmarking in manipulation research: The ycb object and model set and benchmarking protocols, 2015.

S. Caselli, C. Magnanini, and F. Zanichelli, Haptic object recognition with a dextrous hand based on volumetric shape representations, IEEE Int

, Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp.280-287, 1994.

S. Changpinyo, W. Chao, and F. Sha, Predicting visual exemplars of unseen classes for zero-shot learning, IEEE Int. Conf. on Computer Vision (ICCV, pp.3496-3505, 2017.

Y. Cheng, X. Qiao, X. Wang, and Q. Yu, Random forest classifier for zeroshot learning based on relative attribute, IEEE Transactions on Neural Networks and Learning Systems, 2017.

Y. S. Choi, T. Deyle, T. Chen, J. D. Glass, and C. C. Kemp, A list of household objects for robotic retrieval prioritized by people with als, IEEE Int. Conf. on Rehabilitation Robotics, pp.510-517, 2009.

V. Chu, I. Mcmahon, L. Riano, C. G. Mcdonald, Q. He et al., Robotic learning of haptic adjectives through physical interaction, Robotics and Autonomous Systems, vol.63, pp.279-292, 2015.

V. Chu, I. Mcmahon, L. Riano, C. G. Mcdonald, Q. He et al., Using robotic exploratory procedures to learn the meaning of haptic adjectives, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.3048-3055, 2013.

M. Controzzi, C. Cipriani, and M. C. Carrozza, Design of artificial hands: A review, The Human Hand as an Inspiration for Robot Hand Development, pp.219-246, 2014.

T. Corradi, P. Hall, and P. Iravani, Tactile features: recognising touch sensations with a novel and inexpensive tactile sensor, Conference Towards Autonomous Robotic Systems, pp.163-172, 2014.

T. Corradi, P. Hall, and P. Iravani, Bayesian tactile object recognition: learning and recognising objects using a new inexpensive tactile sensor, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.3909-3914, 2015.

T. De-oliveria, A. Cretu, and E. M. Petriu, Multimodal bio-inspired tactile sensing module, IEEE Sens. J, vol.17, pp.1-1, 2017.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet: A large-scale hierarchical image database, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR, pp.248-255, 2009.

A. Drimus, G. Kootstra, A. Bilberg, and D. Kragic, Design of a flexible tactile sensor for classification of rigid and deformable objects, Robotics and Autonomous Systems, vol.62, pp.3-15, 2014.

M. Elhoseiny, B. Saleh, and A. Elgammal, Write a classifier: Zero-shot learning using purely textual descriptions, IEEE Int. Conf. on Computer Vision (ICCV) (2013), pp.2584-2591

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisser-man, The pascal visual object classes (voc) challenge, International journal of computer vision, vol.88, pp.303-338, 2010.

P. Falco, S. Lu, A. Cirillo, C. Natale, S. Pirozzi et al., Crossmodal visuo-tactile object recognition using robotic active exploration, IEEE Int. Conf. on Robotics and Automation (ICRA, pp.5273-5280, 2017.

A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, Describing objects by their attributes, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR, pp.1778-1785, 2009.

L. Fe-fei and . Et-al, A bayesian approach to unsupervised one-shot learning of object categories, Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pp.1134-1141, 2003.

L. Fei-fei, R. Fergus, and P. Perona, One-shot learning of object categories, IEEE transactions on pattern analysis and machine intelligence, vol.28, pp.594-611, 2006.

L. Fei-fei, R. Fergus, and P. Perona, Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories, Computer vision and Image understanding, vol.106, pp.59-70, 2007.
URL : https://hal.archives-ouvertes.fr/hal-02053466

J. Fishel, G. Lin, and G. Loeb, Biotac R product manual. SynTouch LLC, 2013.

Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong, Transductive multiview zero-shot learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, pp.2332-2345, 2015.

Z. Fu, T. Xiang, E. Kodirov, and S. Gong, Zero-shot object recognition by semantic manifold distance, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2015), pp.2635-2644

J. M. Gandarias, . Gómez-de, J. M. Gabriel, and A. García-cerezo, Human and object recognition with a high-resolution tactile sensor, SENSORS, pp.1-3, 2017.

Y. Gao, L. A. Hendricks, K. J. Kuchenbecker, and T. Darrell, Deep learning for tactile understanding from visual and haptic data, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.536-543, 2016.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, In Int. Conf. on Artificial Intelligence and Statistics, pp.249-256, 2010.

D. Goger, N. Gorges, and H. Worn, Tactile sensing for an anthropomorphic robotic hand: Hardware and signal processing, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.895-901, 2009.

A. M. Gordon and J. F. Soechting, Use of tactile afferent information in sequential finger movements, Experimental brain research, vol.107, pp.281-292, 1995.

N. Gorges, S. E. Navarro, D. Göger, and H. Wörn, Haptic object recognition using passive joints and haptic key features, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.2349-2355, 2010.

N. Gorges, S. E. Navarro, and H. Wörn, Haptic object recognition using statistical point cloud features, IEEE International Conference on Advanced Robotics (ICAR), pp.15-20, 2011.

K. Grauman and B. Leibe, Visual object recognition, Synthesis lectures on artificial intelligence and machine learning, vol.5, pp.1-181, 2011.

G. Griffin, A. Holub, and P. Perona, Caltech-256 object category dataset

K. Grill-spector and N. Kanwisher, Visual recognition: As soon as you know it is there, you know what it is, Psychological Science, vol.16, pp.152-160, 2005.

S. Grossberg, Recurrent neural networks. Scholarpedia, vol.8, p.1888, 2013.

H. Gu, S. Fan, H. Zong, M. Jin, and H. Liu, Haptic perception of unknown object by robot hand: Exploration strategy and recognition approach, International Journal of Humanoid Robotics, vol.13, p.1650008, 2016.

P. Güler, Y. Bekiroglu, X. Gratal, K. Pauwels, and D. Kragic, What's in the container? classifying object contents from vision and touch, IEEE/RSJ International Conference on, pp.3961-3968, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, Support vector machines. IEEE Intelligent Systems and their applications, vol.13, pp.18-28, 1998.

P. Hensman and D. Masko, The impact of imbalanced training data for convolutional neural networks. Degree Project in Computer Science, 2015.

J. Hoelscher, J. Peters, and T. Hermans, Evaluation of tactile feature extraction for interactive object recognition, IEEE-RAS Int. Conf. on Humanoid Robots, pp.310-317, 2015.

K. Hosoda and T. Iwase, Robust haptic recognition by anthropomorphic bionic hand through dynamic interaction, Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pp.1236-1241, 2010.

P. Hsiao, F. Chang, and Y. Lin, Learning discriminatively reconstructed source data for object recognition with few examples, IEEE Transactions on Image Processing, vol.25, pp.3518-3532, 2016.

H. Iwata and S. Sugano, Design of human symbiotic robot twendy-one, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.580-586, 2009.

S. Jacobsen, E. Iversen, D. Knutti, R. Johnson, and K. Biggers, Design of the utah/mit dextrous hand, IEEE Int. Conf. on Robotics and Automation (ICRA), vol.3, pp.1520-1532, 1986.

A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz et al., A category-level 3d object dataset: Putting the kinect to work, Consumer Depth Cameras for Computer Vision, pp.141-165, 2013.

D. Jayaraman and K. Grauman, Zero-shot recognition with unreliable attributes, Advances in Neural Information Processing Systems, pp.3464-3472, 2014.

Y. Ji, Y. Yang, X. Xu, and H. T. Shen, One-shot learning based pattern transition map for action early recognition, Signal Processing, vol.143, pp.364-370, 2018.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe: Convolutional architecture for fast feature embedding, ACM Int. Conf. on Multimedia, pp.675-678, 2014.

M. Jin, H. Gu, S. Fan, Y. Zhang, and H. Liu, Object shape recognition approach for sparse point clouds from tactile exploration, IEEE Int. Conf. on Robotics and Biomimetics (ROBIO), pp.558-562, 2013.

R. Johansson and G. Westling, Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects, Experimental brain research, vol.56, pp.550-564, 1984.

M. Johnsson and C. Balkenius, Experiments with proprioception in a selforganizing system for haptic perception, Towards Autonomous Robotic Systems, pp.239-245, 2007.

M. Johnsson and C. Balkenius, Haptic perception with self-organizing anns and an anthropomorphic robot hand, Journal of Robotics, p.2010, 2010.

I. Jolliffe, Principal component analysis, International encyclopedia of statistical science, pp.1094-1096, 2011.

M. Kaboli and G. Cheng, Novel tactile descriptors and a tactile transfer learning technique for active in-hand object recognition via texture properties, IEE-RAS Int. Conf. on Humanoid Robots-Workshop Tactile Sensing for Manipulation: New Progress and Challenges, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01385113

M. Kaboli, R. Walker, and G. Cheng, Re-using prior tactile experience by robotic hands to discriminate in-hand objects via texture properties, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.2242-2247, 2016.

M. Kaboli, R. Walker, G. Cheng, and . Et-al, In-hand object recognition via texture properties with robotic hands, artificial skin, and novel tactile descriptors, IEEE-RAS Int. Conf. on Humanoid Robots, pp.1155-1160, 2015.

P. Kankuekul, A. Kawewong, S. Tangruamsub, and O. Hasegawa, Online incremental attribute-based zero-shot learning, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp.3657-3664, 2012.

Z. Kappassov, J. Corrales, and V. Perdereau, Tactile sensing in dexterous robot hands, Robotics and Autonomous Systems, vol.74, pp.195-220, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01680649

C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda, Learning systems of concepts with an infinite relational model, AAAI, vol.3, p.5, 2006.

E. Keogh and A. Mueen, Curse of dimensionality, Encyclopedia of Machine Learning and Data Mining, pp.314-315, 2017.

E. Kerr, T. M. Mcginnity, and S. Coleman, Material classification based on thermal and surface texture properties evaluated against human performance, IEEE Int. Conf. on Control Automation Robotics & Vision (ICARCV), pp.444-449, 2014.

S. G. Kg,

C. Schunk and . Hand,

J. K. Kim, J. W. Wee, and C. H. Lee, Sensor fusion system for improving the recognition of 3d object, Cybernetics and Intelligent Systems, vol.2, pp.1207-1212, 2004.

D. P. Kingma, J. Ba, and . Adam, A method for stochastic optimization, 2014.

R. Klatzky, S. Lederman, . Human, and . Haptics, New encyclopedia of neuroscience, vol.5, pp.11-18, 2009.

R. L. Klatzky, S. J. Lederman, and D. E. Matula, Haptic exploration in the presence of vision, Journal of Experimental Psychology: Human Perception and Performance, vol.19, p.726, 1993.

R. L. Klatzky, S. J. Lederman, and C. Reed, There's more to touch than meets the eye: The salience of object attributes for haptics with and without vision, Journal of experimental psychology: general, vol.116, p.356, 1987.

T. Kohonen, The self-organizing map, Proceedings of the IEEE, vol.78, pp.1464-1480, 1990.

A. Krizhevsky, Learning multiple layers of features from tiny images, 2009.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, Attribute and simile classifiers for face verification, IEEE Int. Conf. on Computer Vision (ICCV, pp.365-372, 2009.

C. H. Lampert, H. Nickisch, and S. Harmeling, Learning to detect unseen object classes by between-class attribute transfer, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR, pp.951-958, 2009.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, pp.2278-2324, 1998.

S. J. Lederman and R. L. Klatzky, Hand movements: A window into haptic object recognition, Cognitive Psychology, vol.19, pp.342-368, 1987.

S. J. Lederman and R. L. Klatzky, Extracting object properties through haptic exploration, Acta psychologica, vol.84, pp.29-40, 1993.

S. J. Lederman and R. L. Klatzky, Haptic perception: A tutorial, Perception, & Psychophysics, vol.71, pp.1439-1459, 2009.

. Lei, J. Ba, K. Swersky, S. Fidler, and . Et-al, Predicting deep zero-shot convolutional neural networks using textual descriptions, IEEE Int. Conf. on Computer Vision (ICCV) (2015), pp.4247-4255

M. V. Liarokapis, B. Calli, A. J. Spiers, and A. M. Dollar, Unplanned, model-free, single grasp object classification with underactuated hands and force sensors, IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp.5073-5080, 2015.

H. Liu, D. Guo, and F. Sun, Object recognition using tactile measurements: Kernel sparse coding methods, IEEE Trans. on Instrumentation and Measurement, vol.65, pp.656-665, 2016.

H. Liu, F. Sun, B. Fang, and D. Guo, Cross-modal zero-shot-learning for tactile object recognition, IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp.1-9, 2018.

H. Liu, F. Sun, D. Guo, B. Fang, and Z. Peng, Structured outputassociated dictionary learning for haptic understanding, IEEE Transactions on Systems, Man, and Cybernetics, 2017.

H. Liu, Y. Wu, F. Sun, B. Fang, and D. Guo, Weakly paired multimodal fusion for object recognition, IEEE Transactions on Automation Science and Engineering, vol.15, pp.784-795, 2018.

H. Liu, Y. Wu, F. Sun, D. Guo, and B. Fang, Multi-label tactile property analysis, IEEE Int. Conf. on Robotics and Automation (ICRA, pp.366-371, 2017.

S. Luo, W. Mou, K. Althoefer, and H. Liu, Novel tactile-sift descriptor for object shape recognition, IEEE Sensors Journal, vol.15, pp.5001-5009, 2015.

M. Madry, L. Bo, D. Kragic, D. Fox, and . St-hmp, Unsupervised spatiotemporal feature learning for tactile data, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.2262-2269, 2014.

U. Martinez-hernandez, N. F. Lepora, and T. J. Prescott, Active haptic shape recognition by intrinsic motivation with a robot hand, IEEE World Haptics Conference (WHC), pp.299-304, 2015.

K. Matheus and A. M. Dollar, Benchmarking grasping and manipulation: properties of the objects of daily living, IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp.5020-5027, 2010.

M. Meier, M. Schopfer, R. Haschke, and H. Ritter, A probabilistic approach to tactile shape reconstruction, IEEE Transactions on Robotics, vol.27, pp.630-635, 2011.

C. Melchiorri and G. Vassura, Mechanical and control features of the ub hand version 2, Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS'92, pp.7-10, 1992.

T. Mensink, E. Gavves, C. G. Snoek, and . Costa, Co-occurrence statistics for zero-shot classification, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR, pp.2441-2448, 2014.

T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka, Distance-based image classification: Generalizing to new classes at near-zero cost, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, pp.2624-2637, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00817211

G. Metta, G. Sandini, D. Vernon, L. Natale, and F. Nori, The icub humanoid robot: an open platform for research in embodied cognition, Proceedings of the 8th workshop on performance metrics for intelligent systems, pp.50-56, 2008.

G. A. Miller, Wordnet: a lexical database for english, Communications of the ACM, vol.38, pp.39-41, 1995.

A. Mishra, M. Reddy, A. Mittal, and H. A. Murthy, A generative model for zero shot learning using conditional variational autoencoders, 2017.

P. Mittendorfer, E. Yoshida, and G. Cheng, Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot, Advanced Robotics, vol.29, pp.51-67, 2015.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10) (2010), pp.807-814

S. E. Navarro, N. Gorges, H. Wörn, J. Schill, T. Asfour et al., Haptic object recognition for multi-fingered robot hands, IEEE Haptics Symposium (HAPTICS), pp.497-502, 2012.

M. Nilsback and A. Zisserman, Automated flower classification over a large number of classes, Computer Vision, Graphics & Image Processing, pp.722-729, 2008.

H. Orii, S. Tsuji, T. Kouda, and T. Kohama, Tactile texture recognition using convolutional neural networks for time-series data of pressure and 6-axis acceleration sensor, Int. Conf. on Industrial Technology (ICIT, pp.1076-1080, 2017.

D. N. Osherson, J. Stern, O. Wilkie, M. Stob, and E. E. Smith, Default probability, Cognitive Science, vol.15, pp.251-269, 1991.

M. Palatucci and T. M. Mitchell, Classification in very high dimensional problems with handfuls of examples, European Conference on Principles of Data Mining and Knowledge Discovery, pp.212-223, 2007.

M. Palatucci, D. Pomerleau, G. E. Hinton, and T. M. Mitchell, Zero-shot learning with semantic output codes, Advances in Neural Information Processing Systems, pp.1410-1418, 2009.

D. Parikh and K. Grauman, Relative attributes, IEEE Int. Conf. on Computer Vision (ICCV, pp.503-510, 2011.

G. Patterson and J. Hays, Sun attribute database: Discovering, annotating, and recognizing scene attributes, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp.2751-2758, 2012.

Z. Pezzementi, E. Plaku, C. Reyda, and G. D. Hager, Tactile-object recognition from appearance information, IEEE Transactions on Robotics, vol.27, pp.473-487, 2011.

E. Rabin and A. M. Gordon, Tactile feedback contributes to consistency of finger movements during typing, Experimental brain research, vol.155, pp.362-369, 2004.

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, 2015.

J. Read, B. Pfahringer, G. Holmes, and E. Frank, Classifier chains for multi-label classification, Machine Learning, vol.85, pp.333-359, 2011.

S. Reed, Z. Akata, H. Lee, and B. Schiele, Learning deep representations of fine-grained visual descriptions, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR, pp.49-58, 2016.

M. Regoli, N. Jamali, G. Metta, and L. Natale, Controlled tactile exploration and haptic object recognition, IEEE Int. Conf. on Advanced Robotics (ICAR, pp.47-54, 2017.

D. Reynolds, Gaussian mixture models, Encyclopedia of biometrics, pp.827-832, 2015.

E. Rodner and J. Denzler, Learning with few examples by transferring feature relevance, Joint Pattern Recognition Symposium, pp.252-261, 2009.

M. Rohrbach, M. Stark, and B. Schiele, Evaluating knowledge transfer and zero-shot learning in a large-scale setting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp.1641-1648, 2011.

M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele, What helps where-and why? semantic relatedness for knowledge transfer, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp.910-917, 2010.

B. Romera-paredes and P. Torr, An embarrassingly simple approach to zero-shot learning, Int. Conf. on Machine Learning, pp.2152-2161, 2015.

O. Russakovsky and F. Li, Attribute learning in large-scale datasets, European Conf. on Computer Vision (ECCV) (2010), vol.6553, pp.1-14

B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, Labelme: a database and web-based tool for image annotation, International journal of computer vision, vol.77, pp.157-173, 2008.

R. A. Russell, Object recognition by asmarttactile sensor, Proceedings of the Australian Conference on Robotics and Automation, pp.93-101, 2000.

R. Salakhutdinov, J. Tenenbaum, and A. Torralba, One-shot learning with a hierarchical nonparametric bayesian model, Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp.195-206, 2012.

M. Sanchez-fibla, A. Duff, and P. F. Verschure, A sensorimotor account of visual and tactile integration for object categorization and grasping, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.107-112, 2013.

A. Schmitz, Y. Bansho, K. Noda, H. Iwata, T. Ogata et al., Tactile object recognition using deep learning and dropout, IEEE-RAS Int. Conf. on Humanoid Robots, pp.1044-1050, 2014.

A. Schneider, J. Sturm, C. Stachniss, M. Reisert, H. Burkhardt et al., Object identification with tactile sensors using bag-offeatures, IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp.243-248, 2009.

M. Schopfer, H. Ritter, and G. Heidemann, Acquisition and application of a tactile database, IEEE Int. Conf. on Robotics and Automation (ICRA), pp.1517-1522, 2007.
DOI : 10.1109/robot.2007.363539
URL : http://ni.www.techfak.uni-bielefeld.de/files/SchoepferRitterHeidemann2007-AAT.pdf

S. Schulz, C. Pylatiuk, A. Kargov, R. Oberle, and G. Bretthauer, Progress in the development of anthropomorphic fluidic hands for a humanoid robot, 4th IEEE/RAS International Conference on, vol.2, pp.566-575, 2004.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

J. Snell, K. Swersky, and R. Zemel, Prototypical networks for few-shot learning, Advances in Neural Information Processing Systems, pp.4080-4090, 2017.

R. Socher, M. Ganjoo, C. D. Manning, and A. Ng, Zero-shot learning through cross-modal transfer, Advances in neural information processing systems, pp.935-943, 2013.

H. Soh and Y. Demiris, Incrementally learning objects by touch: Online discriminative and generative models for tactile-based recognition, IEEE Transactions on Haptics, vol.7, pp.512-525, 2014.
DOI : 10.1109/toh.2014.2326159

H. Soh, Y. Su, and Y. Demiris, Online spatio-temporal gaussian process experts with application to tactile classification, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4489-4496, 2012.
DOI : 10.1109/iros.2012.6385992
URL : http://spiral.imperial.ac.uk/bitstream/10044/1/12658/4/iros2012.pdf

A. J. Spiers, M. V. Liarokapis, B. Calli, and A. M. Dollar, Singlegrasp object classification and feature extraction with simple robot hands and tactile sensors, IEEE Trans. on Haptics, vol.9, pp.207-220, 2016.
DOI : 10.1109/toh.2016.2521378

Z. Su, J. A. Fishel, T. Yamamoto, and G. E. Loeb, Use of tactile feedback to control exploratory movements to characterize object compliance, Frontiers in Neurorobotics, vol.6, 2012.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions
DOI : 10.1109/cvpr.2015.7298594
URL : http://arxiv.org/pdf/1409.4842

D. Tanaka, T. Matsubara, K. Ichien, and K. Sugimoto, Object manifold learning with action features for active tactile object recognition, IEEE/RSJ Int. Conf. on Robots and Intelligent Systems (IROS), pp.608-614, 2014.
DOI : 10.1109/iros.2014.6942622

T. Tommasi, F. Orabona, and B. Caputo, Safety in numbers: Learning categories from few examples with multi model knowledge transfer, Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp.3081-3088, 2010.
DOI : 10.1109/cvpr.2010.5540064

A. M. Turk, Amazon mechanical turk, 2012.

C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie,

D. Wang, Y. Li, Y. Lin, and Y. Zhuang, Relational knowledge transfer for zero-shot learning, AAAI, vol.2, p.7, 2016.

E. K. Warrington and A. M. Taylor, Two categorical stages of object recognition, Perception, vol.7, pp.695-705, 1978.
DOI : 10.1068/p070695

P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff et al.,

P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff et al., , 2010.

J. R. Wingert, H. Burton, R. J. Sinclair, J. E. Brunstrom, and D. L. Damiano, Tactile sensory abilities in cerebral palsy: deficits in roughness and object discrimination, Developmental Medicine & Child Neurology, vol.50, pp.832-838, 2008.

L. Wolf and I. Martin, Robust boosting for learning from few examples, Computer Vision and Pattern Recognition, vol.1, pp.359-364, 2005.
DOI : 10.1109/cvpr.2005.305

H. Wu, Z. Miao, Y. Wang, and M. Lin, Optimized recognition with few instances based on semantic distance, The Visual Computer, vol.31, pp.367-375, 2015.

Y. Xian, B. Schiele, and Z. Akata, Zero-shot learning -the good, the bad and the ugly, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR, 2017.

J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, Sun database: Large-scale scene recognition from abbey to zoo, Computer vision and pattern recognition (CVPR), pp.3485-3492, 2010.

D. Xu, G. E. Loeb, and J. A. Fishel, Tactile identification of objects using bayesian exploration, IEEE Int. Conf. on Robotics and Automation (ICRA) (2013), pp.3056-3061

J. Yang, H. Liu, F. Sun, and M. Gao, Tactile sequence classification using joint kernel sparse coding, Int. Joint Conf. on Neural Networks (IJCNN), pp.1-6, 2015.

F. X. Yu, L. Cao, R. S. Feris, J. R. Smith, and S. Chang, Designing category-level attributes for discriminative visual recognition, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2013), pp.771-778

X. Yu and Y. Aloimonos, Attribute-based transfer learning for object categorization with zero/one training example, European Conf. on Computer Vision (ECCV), pp.127-140, 2010.

L. Zhang, S. Zhang, F. Jiang, Y. Qi, J. Zhang et al., Bomw: Bag of manifold words for one-shot learning gesture recognition from kinect, 2017.

Y. Zhang, R. Jin, and Z. Zhou, Understanding bag-of-words model: a statistical framework, International Journal of Machine Learning and Cybernetics, vol.1, pp.43-52, 2010.

H. Zheng, L. Fang, M. Ji, M. Strese, Y. Özer et al., Deep learning for surface material classification using haptic and visual information, IEEE Transactions on Multimedia, vol.18, pp.2407-2416, 2016.