Visual genome: connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision 123 (1): 32–73.17 17 Antol, S., Agrawal, A., Lu, J. et al. (2015). VQA: Visual question answering. Proceedings of the IEEE International Conference on Computer Vision, pp. 2425–2433.
18 18 Shenavarmasouleh, F. and Arabnia, H.R. (2020). DRDr: Automatic masking of exudates and microaneurysms caused by diabetic retinopathy using mask R‐CNN and transfer learning. arXiv preprint arXiv:2007.02026.
19 19 Shenavarmasouleh, F., Mohammadi, F.G., Amini, M.H., and Arabnia, H.R. (2020). DRDr II: Detecting the severity level of diabetic retinopathy using mask RCNN and transfer learning. arXiv preprint arXiv:2011.14733.
20 20 Shenavarmasouleh, F. and Arabnia, H. (2019). Causes of misleading statistics and research results irreproducibility: a concise review. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 465–470.
21 21 Held, R. and Hein, A. (1963). Movement‐produced stimulation in the development of visually guided behavior. Journal of Comparative and Physiological Psychology 56 (5): 872.
22 22 Moravec, H. (1984). Locomotion, vision and intelligence.
23 23 Hoffmann, M. and Pfeifer, R. (2012). The implications of embodiment for behavior and cognition: animal and robotic case studies. arXiv preprint arXiv:1202.0440.
24 24 Brooks, R.A. (1991). New approaches to robotics. Science 253 (5025): 1227–1232.
25 25 Collins, S.H., Wisse, M., and Ruina, A. (2001). A three‐dimensional passive‐dynamic walking robot with two legs and knees. The International Journal of Robotics Research 20 (7): 607–615.
26 26 Iida, F. and Pfeifer, R. (2004). Cheap rapid locomotion of a quadruped robot: self‐stabilization of bounding gait. In: Intelligent Autonomous Systems, vol. 8, 642–649. The Netherlands: IOS Press Amsterdam.
27 27 Yamamoto, T. and Kuniyoshi, Y. (2001). Harnessing the robot's body dynamics: a global dynamics approach. Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the Next Millennium (Cat. No. 01CH37180), Volume 1, IEEE, pp. 518–525.
28 28 Bledt, G., Powell, M.J., Katz, B. et al. (2018). MIT cheetah 3: design and control of a robust, dynamic quadruped robot. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 2245–2252.
29 29 Hermann, K.M., Hill, F., Green, S. et al. (2017). Grounded language learning in a simulated 3D world. arXiv preprint arXiv:1706.06551.
30 30 Tenney, I., Das, D., and Pavlick, E. (2019). Bert rediscovers the classical NLP pipeline.
31 31 Pan, Y., Yao, T., Li, H., and Mei, T. (2017). Video captioning with transferred semantic attributes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6504–6512.
32 32 Amirian, S., Rasheed, K., Taha, T.R., and Arabnia, H.R. (2020). Automatic image and video caption generation with deep learning: a concise review and algorithmic overlap. IEEE Access 8: 218386–218400.
33 33 Amirian, S., Rasheed, K., Taha, T.R., and Arabnia, H.R. (2020). Automatic generation of descriptive titles for video clips using deep learning. In: Springer Nature ‐ Research Book Series: Transactions on Computational Science & Computational Intelligence, Hamid R. Arabnia, Springer. 17–28.
34 34 Gao, L., Guo, Z., Zhang, H. et al. (2017). Video captioning with attention‐based LSTM and semantic consistency. IEEE Transactions on Multimedia 19 (9): 2045–2055.
35 35 Yang, Y., Zhou, J., Ai, J. et al. (2018). Video captioning by adversarial LSTM. IEEE Transactions on Image Processing 27 (11): 5600–5611.
36 36 Singh, A., Natarajan, V., Shah, M. et al. (2019). Towards VQA models that can read. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8317–8326.
37 37 Jayaraman, D. and Grauman, K. (2017). Learning image representations tied to egomotion from unlabeled video. International Journal of Computer Vision 125 (1–3): 136–161.
38 38 Jayaraman, D., Gao, R., and Grauman, K. (2018). Shapecodes: self‐supervised feature learning by lifting views to viewgrids. Proceedings of the European Conference on Computer Vision (ECCV), pp. 120–136.
39 39 Gao, R., Feris, R., and Grauman, K. (2018). Learning to separate object sounds by watching unlabeled video. Proceedings of the European Conference on Computer Vision (ECCV), pp. 35–53.
40 40 Parekh, S., Essid, S., Ozerov, A. et al. (2017). Guiding audio source separation by video object information. 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE, pp. 61–65.
41 41 Pu, J., Panagakis, Y., Petridis, S., and Pantic, M. (2017). Audio‐visual object localization and separation using low‐rank and sparsity. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 2901–2905.
42 42 Parekh, S., Essid, S., Ozerov, A. et al. (2017). Motion informed audio source separation. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 6–10.
43 43 Asali, E., Shenavarmasouleh, F., Mohammadi, F. et al. (2020). DeepMSRF: A novel deep multimodal speaker recognition framework with feature selection. ArXiv, abs/2007.06809.
44 44 Aloimonos, J., Weiss, I., and Bandyopadhyay, A. (1988). Active vision. International Journal of Computer Vision 1 (4): 333–356.
45 45 Ballard, D.H. (1991). Animate vision. Artificial Intelligence 48 (1): 57–86.
46 46 Ballard, D.H. and Brown, C.M. (1992). Principles of animate vision. CVGIP: Image Understanding 56 (1): 3–21.
47 47 Bajcsy, R. (1988). Active perception. Proceedings of the IEEE 76 (8): 966–1005.
48 48 Roy, S.D., Chaudhury, S., and Banerjee, S. (2004). Active recognition through next view planning: a survey. Pattern Recognition 37 (3): 429–446.
49 49 Tung, H.‐Y.F., Cheng, R., and Fragkiadaki, K. (2019). Learning spatial common sense with geometry‐aware recurrent networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2595–2603.
50 50 Jayaraman, D. and Grauman, K. (2018). End‐to‐end policy learning for active visual categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (7): 1601–1614.
51 51 Yang, J., Ren, Z., Xu, M. et al. (2019). Embodied visual recognition.
52 52 Das, A., Datta, S., Gkioxari, G. et al. (2018). Embodied question answering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2054–2063.
53 53 Wijmans, E., Datta, S., Maksymets, O. et al. (2019). Embodied question answering in photorealistic environments with point cloud perception. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6659–6668.
54 54 Das, A., Gkioxari, G., Lee, S. et al. (2018). Neural modular control for embodied question answering. arXiv preprint arXiv:1810.11181.
55 55 Gordon, D., Kembhavi, A., Rastegari, M. et al. (2018). IQA: Visual question answering in interactive environments. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4089–4098.
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