SABV-Depth: A biologically inspired deep learning network for monocular depth estimation
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[1] Yuting Yang,et al. The New Generation Brain-Inspired Sparse Learning: A Comprehensive Survey , 2022, IEEE Transactions on Artificial Intelligence.
[2] F. Liu,et al. Evolutionary Dual-Stream Transformer. , 2022, IEEE transactions on cybernetics.
[3] Tak-Wai Hui. RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenes , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] A. Leonardis,et al. Deep neural networks and image classification in biological vision , 2022, Vision Research.
[5] Minggang Gan,et al. Adaptive depth-aware visual relationship detection , 2022, Knowl. Based Syst..
[6] L. Duan,et al. The combined effects of the thalamic feed-forward inhibition and feed-back inhibition in controlling absence seizures , 2022, Nonlinear Dynamics.
[7] J. Álvarez,et al. A-ViT: Adaptive Tokens for Efficient Vision Transformer , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Yonghu Zeng,et al. Self-supervised learning of monocular depth using quantized networks , 2021, Neurocomputing.
[9] D. Tao,et al. A Survey on Vision Transformer , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] Tat-Jun Chin,et al. Auto-Rectify Network for Unsupervised Indoor Depth Estimation , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Alessandro Sarti,et al. LGN-CNN: a biologically inspired CNN architecture , 2019, Neural Networks.
[12] Beiji Zou,et al. Single image depth estimation based on sculpture strategy , 2022, Knowl. Based Syst..
[13] Ning Lv,et al. Self-supervised Monocular Trained Depth Estimation Using Triplet Attention and Funnel Activation , 2021, Neural Processing Letters.
[14] Hasib Zunair,et al. Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentation , 2021, Comput. Biol. Medicine.
[15] Peter Corcoran,et al. An efficient encoder-decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data , 2021, Neural Networks.
[16] Ian Reid,et al. Unsupervised Scale-Consistent Depth Learning from Video , 2021, International Journal of Computer Vision.
[17] Xilin Chen,et al. OCNet: Object Context for Semantic Segmentation , 2021, International Journal of Computer Vision.
[18] Si Wu,et al. A brain-inspired computational model for spatio-temporal information processing , 2021, Neural Networks.
[19] Cordelia Schmid,et al. Segmenter: Transformer for Semantic Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] Anima Pramanik,et al. A real-time video surveillance system for traffic pre-events detection. , 2021, Accident; analysis and prevention.
[21] Pratul P. Srinivasan,et al. IBRNet: Learning Multi-View Image-Based Rendering , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Ryota Kanai,et al. Deep learning and the Global Workspace Theory , 2020, Trends in Neurosciences.
[23] Peter Wonka,et al. AdaBins: Depth Estimation Using Adaptive Bins , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Haitao Zhao,et al. Attention-based context aggregation network for monocular depth estimation , 2019, International Journal of Machine Learning and Cybernetics.
[25] Rosa H. M. Chan,et al. Comparing biological and artificial vision systems: Network measures of functional connectivity , 2020, Neuroscience Letters.
[26] Marcos L. Aranda,et al. Diversity of intrinsically photosensitive retinal ganglion cells: circuits and functions , 2020, Cellular and Molecular Life Sciences.
[27] Chang Shu,et al. Feature-metric Loss for Self-supervised Learning of Depth and Egomotion , 2020, ECCV.
[28] Jimson Mathew,et al. Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences , 2020, 2020 IEEE International Conference on Image Processing (ICIP).
[29] Lanfen Lin,et al. UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[30] Gustavo Carneiro,et al. Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Xiangyu Zhu,et al. Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] J. Changeux,et al. Conscious Processing and the Global Neuronal Workspace Hypothesis , 2020, Neuron.
[33] Rares Ambrus,et al. 3D Packing for Self-Supervised Monocular Depth Estimation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Changde Du,et al. Neural Encoding for Human Visual Cortex with Deep Neural Networks Learning “What” and “Where” , 2019, bioRxiv.
[35] Chunhua Shen,et al. Enforcing Geometric Constraints of Virtual Normal for Depth Prediction , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[36] Takayuki Okatani,et al. Visualization of Convolutional Neural Networks for Monocular Depth Estimation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[37] Dacheng Tao,et al. Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Stefano Soatto,et al. Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Sertac Karaman,et al. FastDepth: Fast Monocular Depth Estimation on Embedded Systems , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[40] James J DiCarlo,et al. Neural population control via deep image synthesis , 2018, Science.
[41] Gabriel J. Brostow,et al. Digging Into Self-Supervised Monocular Depth Estimation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[42] Takayuki Okatani,et al. Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
[43] Nicu Sebe,et al. Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks , 2018, 2018 International Conference on 3D Vision (3DV).
[44] R. Venkatesh Babu,et al. AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] James M. Rehg,et al. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives , 2017, Artificial Intelligence Review.
[46] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[47] Jörg Stückler,et al. Semi-Supervised Deep Learning for Monocular Depth Map Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Oisin Mac Aodha,et al. Unsupervised Monocular Depth Estimation with Left-Right Consistency , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Nassir Navab,et al. Deeper Depth Prediction with Fully Convolutional Residual Networks , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[50] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[51] Rob Fergus,et al. Depth Map Prediction from a Single Image using a Multi-Scale Deep Network , 2014, NIPS.
[52] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[53] Derek Hoiem,et al. Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.
[54] J. Changeux,et al. Ongoing Spontaneous Activity Controls Access to Consciousness: A Neuronal Model for Inattentional Blindness , 2005, PLoS biology.
[55] B. Baars. Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. , 2005, Progress in brain research.