An Exploration of Embodied Visual Exploration
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[1] Devendra Singh Chaplot,et al. Modular Visual Navigation using Active Neural Mapping , 2019 .
[2] Michael Goesele,et al. The Replica Dataset: A Digital Replica of Indoor Spaces , 2019, ArXiv.
[3] Ruslan Salakhutdinov,et al. Learning to Explore using Active Neural SLAM , 2020, ICLR.
[4] Santhosh K. Ramakrishnan,et al. Sidekick Policy Learning for Active Visual Exploration , 2018, ECCV.
[5] Jitendra Malik,et al. Habitat: A Platform for Embodied AI Research , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[6] Mubarak Shah,et al. UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.
[7] Thomas A. Funkhouser,et al. MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments , 2017, ArXiv.
[8] Pierre-Yves Oudeyer,et al. Intrinsic Motivation Systems for Autonomous Mental Development , 2007, IEEE Transactions on Evolutionary Computation.
[9] Sonia Chernova,et al. Are We Making Real Progress in Simulated Environments? Measuring the Sim2Real Gap in Embodied Visual Navigation , 2019, ArXiv.
[10] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[11] Yiannis Aloimonos,et al. Active vision , 2004, International Journal of Computer Vision.
[12] Rahul Sukthankar,et al. Cognitive Mapping and Planning for Visual Navigation , 2017, International Journal of Computer Vision.
[13] Jürgen Schmidhuber,et al. Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.
[14] Xinlei Chen,et al. Embodied Visual Recognition , 2019, ArXiv.
[15] Marc Pollefeys,et al. Episodic Curiosity through Reachability , 2018, ICLR.
[16] Abhinav Gupta,et al. Scaling and Benchmarking Self-Supervised Visual Representation Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[17] Tinne Tuytelaars,et al. Where to Look Next: Unsupervised Active Visual Exploration on 360° Input , 2019, ArXiv.
[18] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[19] Yi Sun,et al. Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments , 2011, AGI.
[20] Filip De Turck,et al. #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning , 2016, NIPS.
[21] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[22] Amos J. Storkey,et al. Exploration by Random Network Distillation , 2018, ICLR.
[23] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Yiming Yang,et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.
[25] Jitendra Malik,et al. Generic 3D Representation via Pose Estimation and Matching , 2016, ECCV.
[26] John K. Tsotsos,et al. Active object recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[27] Nils J. Nilsson,et al. A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..
[28] Javier R. Movellan,et al. Deep Q-learning for Active Recognition of GERMS: Baseline performance on a standardized dataset for active learning , 2015, BMVC.
[29] Alan Yuille,et al. Active Vision , 2014, Computer Vision, A Reference Guide.
[30] Pierre-Yves Oudeyer,et al. Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress , 2012, NIPS.
[31] Jitendra Malik,et al. Unifying Map and Landmark Based Representations for Visual Navigation , 2017, ArXiv.
[32] Stefan Lee,et al. Embodied Question Answering , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] R. Bajcsy. Active perception , 1988, Proc. IEEE.
[35] Xinlei Chen,et al. Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation , 2020, ECCV.
[36] Jitendra Malik,et al. On Evaluation of Embodied Navigation Agents , 2018, ArXiv.
[37] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[38] Tom Schaul,et al. Unifying Count-Based Exploration and Intrinsic Motivation , 2016, NIPS.
[39] Stefan Lee,et al. Neural Modular Control for Embodied Question Answering , 2018, CoRL.
[40] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[41] Jana Kosecka,et al. A dataset for developing and benchmarking active vision , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[42] Vladlen Koltun,et al. Benchmarking Classic and Learned Navigation in Complex 3D Environments , 2019, ArXiv.
[43] Santhosh K. Ramakrishnan,et al. Emergence of exploratory look-around behaviors through active observation completion , 2019, Science Robotics.
[44] Marc G. Bellemare,et al. Count-Based Exploration with Neural Density Models , 2017, ICML.
[45] Stefan Lee,et al. Embodied Question Answering in Photorealistic Environments With Point Cloud Perception , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Wolfram Burgard,et al. Information Gain-based Exploration Using Rao-Blackwellized Particle Filters , 2005, Robotics: Science and Systems.
[47] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[48] Brian Yamauchi,et al. A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.
[49] Pieter Abbeel,et al. Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.
[50] Matthias Nießner,et al. Matterport3D: Learning from RGB-D Data in Indoor Environments , 2017, 2017 International Conference on 3D Vision (3DV).
[51] Rahul Sukthankar,et al. Cognitive Mapping and Planning for Visual Navigation , 2017, International Journal of Computer Vision.
[52] Silvio Savarese,et al. Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Ali Farhadi,et al. AI2-THOR: An Interactive 3D Environment for Visual AI , 2017, ArXiv.
[54] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[55] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[56] Dana H. Ballard,et al. Animate Vision , 1991, Artif. Intell..
[57] Abhinav Gupta,et al. Beyond Games: Bringing Exploration to Robots in Real-world , 2018 .
[58] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[59] Roozbeh Mottaghi,et al. ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects , 2020, ArXiv.
[60] James Bergstra,et al. Benchmarking Reinforcement Learning Algorithms on Real-World Robots , 2018, CoRL.
[61] Alexei A. Efros,et al. Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[62] Ali Farhadi,et al. Visual Semantic Planning Using Deep Successor Representations , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[63] Xin Zhang,et al. End to End Learning for Self-Driving Cars , 2016, ArXiv.
[64] Deepak Pathak,et al. Self-Supervised Exploration via Disagreement , 2019, ICML.
[65] Jürgen Schmidhuber,et al. A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots , 2016, IEEE Robotics and Automation Letters.
[66] Silvio Savarese,et al. Joint 2D-3D-Semantic Data for Indoor Scene Understanding , 2017, ArXiv.
[67] W. Lovejoy. A survey of algorithmic methods for partially observed Markov decision processes , 1991 .
[68] Daniel L. K. Yamins,et al. Learning to Play with Intrinsically-Motivated Self-Aware Agents , 2018, NeurIPS.
[69] Michael L. Littman,et al. An analysis of model-based Interval Estimation for Markov Decision Processes , 2008, J. Comput. Syst. Sci..
[70] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[71] Kristen Grauman,et al. Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[72] Fabio Viola,et al. The Kinetics Human Action Video Dataset , 2017, ArXiv.
[73] Andrea Vedaldi,et al. MapNet: An Allocentric Spatial Memory for Mapping Environments , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[74] Jitendra Malik,et al. Gibson Env: Real-World Perception for Embodied Agents , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[75] Alexei A. Efros,et al. Large-Scale Study of Curiosity-Driven Learning , 2018, ICLR.
[76] Qi Wu,et al. Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[77] Kristen Grauman,et al. End-to-End Policy Learning for Active Visual Categorization , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[78] Leslie Pack Kaelbling,et al. Acting under uncertainty: discrete Bayesian models for mobile-robot navigation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.
[79] Vladlen Koltun,et al. Semi-parametric Topological Memory for Navigation , 2018, ICLR.
[80] Deva Ramanan,et al. Learning to Move with Affordance Maps , 2020, ICLR.