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Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects

arXiv 2109.03391 ↗First posted 8 Sep 2021cs.AIcited by 0

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Abstract (arXiv)

Visual sensation and perception refers to the process of sensing, organizing, identifying, and interpreting visual information in environmental awareness and understanding. Computational models inspired by visual perception have the characteristics of complexity and diversity, as they come from many subjects such as cognition science, information science, and artificial intelligence. In this paper, visual perception computational models oriented deep learning are investigated from the biological visual mechanism and computational vision theory systematically. Then, some points of view about the prospects of the visual perception computational models are presented. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends. Through this survey, it will provide a comprehensive reference for research in this direction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 134 references that are in the map. First posted on arXiv 8 Sep 2021.

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28 of 134 references.

References · 28

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  3. 2014Recurrent Models of Visual Attentioncited by 344
  4. 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
  5. 2016Attend, Infer, Repeat: Fast Scene Understanding with Generative Modelscited by 209
  6. 2020Deep Multimodal Fusion by Channel Exchangingcited by 117
  7. 2017Visual Interaction Networkscited by 94
  8. 2017A Survey of Neuromorphic Computing and Neural Networks in Hardwarecited by 82
  9. 2017Learning Video Object Segmentation with Visual Memorycited by 66
  10. 2019Learning Perceptual Inference by Contrastingcited by 57
  11. 2015Gated Feedback Recurrent Neural Networkscited by 54
  12. 2018Predicting the Computational Cost of Deep Learning Modelscited by 49
16 more
  1. 2016Neuromorphic Deep Learning Machinescited by 38
  2. 2021Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transportcited by 28
  3. 2019End-to-End Pixel-Based Deep Active Inference for Body Perception and Actioncited by 24
  4. 2019DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series predictioncited by 20
  5. 2014Deep Networks with Internal Selective Attention through Feedback Connectionscited by 17
  6. 2018Top-Down Feedback for Crowd Counting Convolutional Neural Networkcited by 14
  7. 2019Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Networkcited by 13
  8. 2017Brain Inspired Cognitive Model with Attention for Self-Driving Carscited by 10
  9. 2019Defining Image Memorability using the Visual Memory Schemacited by 10
  10. 2014Attentional Neural Network: Feature Selection Using Cognitive Feedbackcited by 8
  11. 2018SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detectioncited by 6
  12. 2016X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasetscited by 5
  13. 2017Integrating both Visual and Audio Cues for Enhanced Video Captioncited by 5
  14. 2019A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Predictioncited by 5
  15. 2015Salient Structure Detection by Context-Guided Visual Searchcited by 3
  16. 2020Task-adaptive Asymmetric Deep Cross-modal Hashingcited by 1

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