Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?

By Norbert Krüger, P. Janssen, Sinan Kalkan, M. Lappe, Ales Leonardis, Justus H. Piater, Antonio Jose Rodríguez-Sánchez, Laurenz Wiskott. 2013

389 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…Hung, Kreiman, Poggio, & DiCarlo, 2005; Kobatake & Tanaka, 1994; Kriegeskorte et al., 2008; Lennie & Movshon, 2005; Logothetis, Pauls, & Poggio, 1995; Perrett, Hietanen, Oram, Benson, & Rolls, 1992; Perrett, Rolls, & Caan, 1982); compare a computer vision-oriented survey (Kruger et al., 2013).”
Generative Adversarial Networks for Hyperspectral Image Classification (2018, background): “, CNN) [36], which is inspired by neuroscience [37], utilizes two important strategies which make it different from other deep learning methods.”
Learning Race from Face: A Survey (2014, background): “in complex environment, motion detection and human behavior analysis [230].”
Introspective perception: Learning to predict failures in vision systems (2016, background): “This architecture is reminiscent of the human visual system processes which uses the ventral pathway for processing the spatial information [27], such as shape and color, while the dorsal pathway is responsible for processing the motion information [28].”
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, background): “system of primate human is characterized by a sequence of different levels of processing (on the order of 10), and this kind of learning system performs very well in the tasks of object recognition [25].”
A deep learning framework for hyperspectral image classification using spatial pyramid pooling (2016, background): “Human brains can achieve excellent performance in many tasks such as face recognition because it has a multilayer structure from retina to cortex (Kruger et al. 2013).”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, positive): “This design is inspired by the hierarchical information processing observed in the primate visual system [30, 31].”
Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, positive): “Based on neuroscience, human brains process information with multiple stages from retina to cortex, which leads to high performance on object recognition [31].”
Deep learning in vision-based static hand gesture recognition (2017, background): “Generally, deep networks are trained in a layer-wise fashion and rely on more distributed and hierarchical learning of features as it is found in the human visual cortex [17]; these allow the representation of highly nonlinear functions, discovery of more interesting features in training data and be”
A survey on deep learning for big data (2018, background): “Deep learning has achieved state-of-the-art performance in a broad of applications such as computer vision [15,16], speech recognition [17,18] and text understanding [19,20].”
Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences (2015, background): “The brain also utilizes a hierarchy structure, especially in the visual cortex [1, 2].”
Latent Hierarchical Model of Temporal Structure for Complex Activity Classification (2014, background): “Hierarchical tree-structured model is biologically inspired by the brain architecture and vision system [36], [37].”
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, methodology): “After that filtering, the processed features are concatenated in the inferotemporal area during the cognitive process [30].”