Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
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Mubarak Shah | Simone Palazzo | Concetto Spampinato | Daniela Giordano | Isaak Kavasidis | M. Shah | Joseph Schmidt | S. Palazzo | D. Giordano | C. Spampinato | I. Kavasidis
[1] Jiashi Feng,et al. Multimodal Learning and Reasoning for Visual Question Answering , 2017, NIPS.
[2] Tomoyasu Horikawa,et al. Generic decoding of seen and imagined objects using hierarchical visual features , 2015, Nature Communications.
[3] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[4] C. Connor,et al. Neural representations for object perception: structure, category, and adaptive coding. , 2011, Annual review of neuroscience.
[5] Trevor Darrell,et al. Captioning Images with Diverse Objects , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Anina N. Rich,et al. The Representation of Color across the Human Visual Cortex: Distinguishing Chromatic Signals Contributing to Object Form Versus Surface Color. , 2016, Cerebral cortex.
[7] Desney S. Tan,et al. Combining brain computer interfaces with vision for object categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[9] Vassilis Athitsos,et al. Cognitive Analysis of Working Memory Load from Eeg, by a Deep Recurrent Neural Network , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[10] Nikolaus Kriegeskorte,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[11] Qi Zhao,et al. SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[12] A. Clark. Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.
[13] J. Peirce. Understanding mid-level representations in visual processing. , 2015, Journal of vision.
[14] J. Kaiser,et al. Human gamma-frequency oscillations associated with attention and memory , 2007, Trends in Neurosciences.
[15] Tong Zhang,et al. Spatial–Temporal Recurrent Neural Network for Emotion Recognition , 2017, IEEE Transactions on Cybernetics.
[16] Andrew Owens,et al. Ambient Sound Provides Supervision for Visual Learning , 2016, ECCV.
[17] J. Gallant,et al. Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies , 2011, Current Biology.
[18] Walter J. Scheirer,et al. Perceptual Annotation: Measuring Human Vision to Improve Computer Vision , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Sergio Gomez Colmenarejo,et al. Hybrid computing using a neural network with dynamic external memory , 2016, Nature.
[20] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[21] O. Bertrand,et al. Oscillatory gamma activity in humans and its role in object representation , 1999, Trends in Cognitive Sciences.
[22] Robert Oostenveld,et al. The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.
[23] King-Sun Fu,et al. IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Jack L. Gallant,et al. Natural Scene Statistics Account for the Representation of Scene Categories in Human Visual Cortex , 2013, Neuron.
[25] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[26] Andrew Zisserman,et al. Look, Listen and Learn , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[27] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[28] Dwight J. Kravitz,et al. A new neural framework for visuospatial processing , 2011, Nature Reviews Neuroscience.
[29] Karl J. Friston,et al. Canonical Microcircuits for Predictive Coding , 2012, Neuron.
[30] Antonio Torralba,et al. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.
[31] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Bernt Schiele,et al. Learning Deep Representations of Fine-Grained Visual Descriptions , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[34] N. Yeung,et al. The roles of cortical oscillations in sustained attention , 2015, Trends in Cognitive Sciences.
[35] Ruslan Salakhutdinov,et al. Generating Images from Captions with Attention , 2015, ICLR.
[36] Honglak Lee,et al. Improved Multimodal Deep Learning with Variation of Information , 2014, NIPS.
[37] Noel E. O'Connor,et al. Shallow and Deep Convolutional Networks for Saliency Prediction , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Alice Mado Proverbio,et al. CORRIGENDUM: When a photograph can be heard: Vision activates the auditory cortex within 110 ms , 2013, Scientific Reports.
[39] Bernt Schiele,et al. Generative Adversarial Text to Image Synthesis , 2016, ICML.
[40] Lina Yao,et al. Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface , 2017, AAAI.
[41] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[42] Juhan Nam,et al. Multimodal Deep Learning , 2011, ICML.
[43] S. Palazzo,et al. Deep Learning Human Mind for Automated Visual Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Mubarak Shah,et al. Generative Adversarial Networks Conditioned by Brain Signals , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[45] Deborah Silver,et al. Feature Visualization , 1994, Scientific Visualization.
[46] DarrellTrevor,et al. Long-Term Recurrent Convolutional Networks for Visual Recognition and Description , 2017 .
[47] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Mubarak Shah,et al. Brain2Image: Converting Brain Signals into Images , 2017, ACM Multimedia.
[49] Bart Thomee,et al. New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative , 2010, MIR '10.
[50] J. Bullier. Integrated model of visual processing , 2001, Brain Research Reviews.
[51] Walter J. Scheirer,et al. Using human brain activity to guide machine learning , 2017, Scientific Reports.
[52] Yitong Li,et al. Targeting EEG/LFP Synchrony with Neural Nets , 2017, NIPS.
[53] Ha Hong,et al. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream , 2013, NIPS.
[54] S. Treue. Visual attention: the where, what, how and why of saliency , 2003, Current Opinion in Neurobiology.
[55] Eugenio Culurciello,et al. Deep Predictive Coding Network for Object Recognition , 2018, ICML.
[56] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] C. Koch,et al. A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.
[58] Khan M. Iftekharuddin,et al. Deep recurrent neural network for seizure detection , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[59] Brent Lance,et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces , 2016, Journal of neural engineering.
[60] S. Luck. An Introduction to the Event-Related Potential Technique , 2005 .
[61] Shouqian Sun,et al. Single-trial EEG classification of motor imagery using deep convolutional neural networks , 2017 .
[62] Antonio Torralba,et al. SoundNet: Learning Sound Representations from Unlabeled Video , 2016, NIPS.
[63] Pulkit Grover,et al. Very high density EEG elucidates spatiotemporal aspects of early visual processing , 2017, bioRxiv.
[64] Tiago H. Falk,et al. Deep learning-based electroencephalography analysis: a systematic review , 2019, Journal of neural engineering.
[65] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[66] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[68] Ali Borji,et al. Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study , 2013, IEEE Transactions on Image Processing.
[69] Chuang Gan,et al. The Sound of Pixels , 2018, ECCV.
[70] A. Torralba,et al. The role of context in object recognition , 2007, Trends in Cognitive Sciences.
[71] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.