Deep learning for tactile understanding from visual and haptic data

ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Going deeper with convolutions

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Fully convolutional networks for semantic segmentation

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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Neural Machine Translation by Jointly Learning to Align and Translate

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Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

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Hearing lips and seeing voices

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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

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Recurrent neural network based language model

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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

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Coding and use of tactile signals from the fingertips in object manipulation tasks

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Towards End-To-End Speech Recognition with Recurrent Neural Networks

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Learning Rich Features from RGB-D Images for Object Detection and Segmentation

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Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition

Abdel‐Hamid, O., Mohamed, A., Jiang, H., & Penn, G. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288864 · Cited by 892 · see citing works

Material recognition in the wild with the Materials in Context Database

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Grammar as a Foreign Language

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Analyzing the Performance of Multilayer Neural Networks for Object Recognition

Agrawal, P., Girshick, R., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_22 · Cited by 427 · see citing works

EmoNets: Multimodal deep learning approaches for emotion recognition in video

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Bayesian Exploration for Intelligent Identification of Textures

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Functional imaging of human crossmodal identification and object recognition

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Biomimetic tactile sensor array

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Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks

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Robotic learning of haptic adjectives through physical interaction

Chu, V. C.-M., McMahon, I. T., Riano, L., McDonald, C. G., He, Q. L., Perez-Tejada, J. M., Arrigo, M., Darrell, T., & Kuchenbecker, K. J. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.09.021 · Cited by 142 · see citing works

Using robotic exploratory procedures to learn the meaning of haptic adjectives

Chu, V. C.-M., McMahon, I. T., Riano, L., et al. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631000 · Cited by 106 · see citing works

Development of a Texture Sensor Emulating the Tissue Structure and Perceptual Mechanism of Human Fingers

Mukaibo, Y., Shirado, H., Konyo, M., & Maéno, T. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570499 · Cited by 99 · see citing works

Quantification and evaluation of human tactile sense towards fabrics

Pan, N. (2006) · International Journal of Design & Nature · doi:10.2495/d&n-v1-n1-48-60 · Cited by 97 · see citing works

Visual, haptic and cross-modal recognition of objects and scenes

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A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers

Griffith, S., Sinapov, J., Sukhoy, V., & Stoytchev, A. (2011) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2011.2157504 · Cited by 45 · see citing works

EmoNets: Multimodal deep learning approaches for emotion recognition in videoPreprint

Kahou, S. E., Bouthillier, X., Lamblin, P., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.01800 · Cited by 41 · see citing works

Fabric Handle Clusters Based on Fuzzy Clustering Algorithm

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Learning the signatures of the human grasp using a scalable tactile glove

Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., & Matusik, W. (2019) · Nature · doi:10.1038/s41586-019-1234-z · Cited by 1,162 · see citing works

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0520-2 · Cited by 1,027 · see citing works

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

Lee, M. A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Fei-Fei, L., Garg, A., & Bohg, J. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793485 · Cited by 349 · see citing works

Haptic Codecs for the Tactile Internet

Steinbach, E., Strese, M., Eid, M., Liu, X., Bhardwaj, A., Liu, Q., Al-Ja'Afreh, M., Mahmoodi, T., Hassen, R., El Saddik, A., et al. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2867835 · Cited by 168 · see citing works

AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence

Clune, J. (2019) · arXiv · doi:10.48550/arxiv.1905.10985 · Cited by 82 · see citing works

Convolutional neural network and long short-term memory models for ice-jam predictions

Madaeni, F., Chokmani, K., Lhissou, R., Homayouni​​​​​​​, S., Gauthier, Y., & Tolszczuk-Leclerc, S. (2022) · The Cryosphere · doi:10.5194/tc-16-1447-2022 · Cited by 51 · see citing works

Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors

Lee, M. A., Tan, M., Zhu, Y., & Bohg, J. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561847 · Cited by 24 · see citing works

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works

Analyzing User Behavior within a Haptic System

Johnson, S., Li, Y., Nam, C. S., & Yamaguchi, T. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21605-3_7 · Cited by 3 · see citing works

Effect of haptic feedback on pseudo-haptic feedback for arm display

Tatezono, M., Sato, K., Minamizawa, K., et al. (2009) · Cited by 15 · see citing works

햅틱 지식지도(I) : 촉각 연구의 동향 분석

김문조, & 지승학 (2012) · 영상문화

Haptics in minimally invasive surgery – a review

der Putten, E. P. W.-V., Goossens, R., JAKIMOWICZ, J. J., & Dankelman, J. (2008) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645700701820242 · Cited by 328 · see citing works

Designing a developable surface with haptic force feedback

Tsai, W.-L., Lo, C., & Chu, C. (2008) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1243/09544054jem1354 · Cited by 1 · see citing works