Similar articles

Abstract: Perception is the interaction interface between an intelligent system and the real world. Without sophisticated and flexible perceptual capabilities, it is impossible to create advanced artificial intelligence (AI) systems. For the next-generation AI, called ‘AI 2.0’, one of the most significant features will be that AI is empowered with intelligent perceptual capabilities, which can simulate human brain’s mechanisms and are likely to surpass human brain in terms of performance. In this paper, we briefly review the state-of-the-art advances across different areas of perception, including visual perception, auditory perception, speech perception, and perceptual information processing and learning engines. On this basis, we envision several R&D trends in intelligent perception for the forthcoming era of AI 2.0, including: (1) human-like and transhuman active vision; (2) auditory perception and computation in an actual auditory setting; (3) speech perception and computation in a natural interaction setting; (4) autonomous learning of perceptual information; (5) large-scale perceptual information processing and learning platforms; and (6) urban omnidirectional intelligent perception and reasoning engines. We believe these research directions should be highlighted in the future plans for AI 2.0.

AI2.0时代的类人与超人感知：研究综述与趋势展望

[1]Amodei, D., Anubhai, R., Battenberg, E., et al., 2015. Deep Speech 2: end-to-end speech recognition in English and Mandarin. arXiv:1512.02595.

[2]Bear, M.F., Connors, B.W., Paradiso, M.A., 2001. Neuroscience. Lippincott Williams and Wilkins, Maryland, p.208.

[3]Bruna, J., Mallat, S., 2013. Invariant scattering convolution networks. IEEE Trans. Patt. Anal. Mach. Intell., 35(8): 1872-1886.

[4]Candès, E., Romberg, J., Tao, T., 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52(2):489-509.

[5]Deng, J., Dong, W., Socher, R., et al., 2009. ImageNet: a large-scale hierarchical image database. IEEE Conf. on Computer Vision and Pattern Recognition, p.248-255.

[6]Duarte, M., Davenport, M., Takhar, D., et al., 2008. Single-pixel imaging via compressive sampling. IEEE Signal Proc. Mag., 25(2):83-91.

[7]Han, J., Shao, L., Xu, D., et al., 2013. Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Trans. Cybern., 43(5):1318-1334.

[8]Hinton, G., Deng, L., Yu, D., et al., 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Proc. Mag., 29(6):82-97.

[9]Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neur. Comput., 9(8):1735-1780.

[10]Hou, Y.Z., Jiao, L.F., 2014. Survey of smart city construction study from home and abroad. Ind. Sci. Trib., 13(24):94-97 (in Chinese).

[11]Jiang, H., Huang, G., Wilford, P., 2014. Multi-view in lensless compressive imaging. Apsipa Trans. Signal Inform. Proc., 3(15):1-10.

[12]Kadambi, A., Whyte, R., Bhandari, A., et al., 2013. Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles. ACM Trans. Graph., 32(6):1-10.

[13]Kale, P.V., Sharma, S.D., 2014. A review of securing home using video surveillance. Int. J. Sci. Res., 3(5):1150-1154.

[14]Kendrick, K.M., 1998. Intelligent perception. Appl. Animal Behav. Sci., 57(3-4):213-231.

[15]King, S., 2014. Measuring a decade of progress in text-to-speech. Loquens, 1(1):e006.

[16]Krizhevsk, A., Sutskever, I., Hinton, G., 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, p.1097-1105.

[17]Lacey, G., Taylor, G.W., Areibi, S., 2016. Deep learning on FPGAs: past, present, and future. arXiv:1602.04283.

[18]LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature, 521(7553):436-444.

[19]Li, T., Chang, H., Wang, M., et al., 2015. Crowded scene analysis: a survey. IEEE Trans. Circ. Syst. Video Technol., 25(3):367-386.

[20]Ling, Z.H., Kang, S.Y., Zen, H., et al., 2015. Deep learning for acoustic modeling in parametric speech generation: a systematic review of existing techniques and future trends. IEEE Signal Proc. Mag., 32(3):35-52.

[21]Lippmann, R.P., 1997. Speech recognition by machines and humans. Speech Commun., 22(1):1-15.

[22]Litovsky, R.Y., Colburn, H.S., Yost, W.A., et al., 1999. The precedence effect. J. Acoust. Soc. Am., 106:1633-1654.

[23]Mahendran, A., Vedaldi, A., 2015. Understanding deep image representations by inverting them. IEEE Int. Conf. on Computer Vision Pattern Recognition, p.5188-5196.

[24]Makhoul, J., 2016. A 50-year retrospective on speech and language processing. Int. Conf. on Interspeech, p.1.

[25]Mattys, S.L., Davis, M.H., Bradlow, A.R., et al., 2012. Speech recognition in adverse conditions: a review. Lang. Cogn. Proc., 27:953-978.

[26]McMackin, L., Herman, M.A., Chatterjee, B., et al., 2012. A high-resolution SWIR camera via compressed sensing. SPIE, 8353:835303.

[27]Mountcastle, V., 1978. An organizing principle for cerebral function: the unit model and the distributed system. In: Edelman, G.M., Mountcastle, V.B. (Eds.), The Mindful Brain. MIT Press, Cambridge.

[28]Musialski, P., Wonka, P., Aliaga, D.G., et al., 2013. A survey of urban reconstruction. Comput. Graph. Forum, 32(6): 146-177.

[29]Ngiam, J., Khosla, A., Kim, M., et al., 2011. Multimodal deep learning. 28th In. Conf. on Machine Learning, p.689-696.

[30]Niwa, K., Koizumi, Y., Kawase, T., et al., 2016. Pinpoint extraction of distant sound source based on DNN mapping from multiple beamforming outputs to prior SNR. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.435-439.

[31]Oord, A., Dieleman, S., Zen, H., et al., 2016. WaveNet: a generative model for raw audio. arXiv:1609.03499.

[32]Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[33]Pratt, G., Manzo, J., 2013. The DARPA robotics challenge. IEEE Robot. Autom. Mag., 20(2):10-12.

[34]Priano, F.H., Armas, R.L., Guerra, C.F., 2016. A model for the smart development of island territories. Int. Conf. on Digital Government Research, p.465-474.

[35]Raina, R., Battle, A., Lee, H., et al., 2007. Self-taught learning: transfer learning from unlabeled data. 24th Int. Conf. on Machine Learning, p.759-766.

[36]Robinson, E.A., Treitel, S., 1967. Principles of digital Wiener filtering. Geophys. Prospect., 15(3):311-332.

[37]Roy, R., Kailath, T., 1989. ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Acoust. Speech Signal Process., 37(7):984-995.

[38]Salakhutdinov, R., Hinton, G., 2009. Deep Boltzmann machines. J. Mach. Learn. Res., 5:448-455.

[39]Saon, G., Kuo, H.K.J., Rennie, S., et al., 2015. The IBM 2015 English conversational telephone speech recognition system. arXiv:1505.05899.

[40]Seide, F., Li, G., Yu, D., 2011. Conversational speech transcription using context-dependent deep neural networks. Int. Conf. on Interspeech, p.437-440.

[41]Soltau, H., Saon, G., Sainath, T.N., 2014. Joint training of convolutional and nonconvolutional neural networks. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.5572-5576.

[42]Song, T., Chen, J., Zhang, D.B., et al., 2016. A sound source localization algorithm using microphone array with rigid body. Int. Congress on Acoustics, p.1-8.

[43]Suzuki, L.R., 2015. Data as Infrastructure for Smart Cities. PhD Thesis, University College London, London, UK.

[44]Tadano, R., Pediredla, A., Veeraraghavan, A., 2015. Depth selective camera: a direct, on-chip, programmable technique for depth selectivity in photography. Int. Conf. on Computer Vision, p.3595-3603.

[45]Tokuda, K., Nankaku, Y., Toda, T., et al., 2013. Speech synthesis based on hidden Markov models. Proc. IEEE, 101(5):1234-1252.

[46]Turk, M., Pentland, A., 1991. Eigenfaces for recognition. J. Cogn. Neurosci., 3(1):71-86.

[47]Veselý, K., Ghoshal, A., Burget, L., et al., 2013. Sequence-discriminative training of deep neural networks. Int. Conf. on Interspeech, p.2345-2349.

[48]Wang, W., Xu, S., Xu, B., 2016. First step towards end-to-end parametric TTS synthesis: generating spectral parameters with neural attention. Int. Conf. on Interspeech, p.2243-2247.

[49]Xiong, W., Droppo, J., Huang, X., et al., 2016. Achieving human parity in conversational speech recognition. arXiv:1610.05256.

[50]Zhang, J.P., Wang, F.Y., Wang, K.F., et al., 2011. Data-driven intelligent transportation systems: a survey. IEEE Trans. Intell. Transp. Syst., 12(4):1624-1639.

[51]Zheng, L., Yang, Y., Hauptmann, A.G., 2016. Person re-identification: past, present and future. arXiv:1610. 02984.