Modelling the Human Intuition to Complete the Missing Information in Images for Convolutional Neural Networks

• URL: http://arxiv.org/abs/2407.09236v1
• Date: Fri, 12 Jul 2024 13:05:27 GMT
• Title: Modelling the Human Intuition to Complete the Missing Information in Images for Convolutional Neural Networks
• Authors: Robin Koç, Fatoş T. Yarman Vural,
• Abstract summary: Experimental psychology reveals many types of intuition, which depend on state of the human mind. We focus on visual intuition, useful for completing missing information during visual cognitive tasks. In this study, we attempt to model intuition and incorporate this formalism to improve the performance of the Convolutional Neural Networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we attempt to model intuition and incorporate this formalism to improve the performance of the Convolutional Neural Networks. Despite decades of research, ambiguities persist on principles of intuition. Experimental psychology reveals many types of intuition, which depend on state of the human mind. We focus on visual intuition, useful for completing missing information during visual cognitive tasks. First, we set up a scenario to gradually decrease the amount of visual information in the images of a dataset to examine its impact on CNN accuracy. Then, we represent a model for visual intuition using Gestalt theory. The theory claims that humans derive a set of templates according to their subconscious experiences. When the brain decides that there is missing information in a scene, such as occlusion, it instantaneously completes the information by replacing the missing parts with the most similar ones. Based upon Gestalt theory, we model the visual intuition, in two layers. Details of these layers are provided throughout the paper. We use the MNIST data set to test the suggested intuition model for completing the missing information. Experiments show that the augmented CNN architecture provides higher performances compared to the classic models when using incomplete images.

Related papers

• Brain Decodes Deep Nets [9.302098067235507]
  We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images.
  arXiv  Detail & Related papers  (2023-12-03T04:36:04Z)
• Probing Graph Representations [77.7361299039905]
  We use a probing framework to quantify the amount of meaningful information captured in graph representations. Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models. We advocate for probing as a useful diagnostic tool for evaluating graph-based models.
  arXiv  Detail & Related papers  (2023-03-07T14:58:18Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks [61.60177890353585]
  Deep convolutional neural networks (CNNs) have been shown to provide excellent models for its functional analogue in the brain, the ventral stream in visual cortex. Here we consider some prominent statistical patterns that are known to exist in the internal representations of either CNNs or the visual cortex. We show that CNNs and visual cortex share a similarly tight relationship between dimensionality expansion/reduction of object representations and reformatting of image information.
  arXiv  Detail & Related papers  (2022-05-27T08:06:40Z)
• Perception Visualization: Seeing Through the Eyes of a DNN [5.9557391359320375]
  We develop a new form of explanation that is radically different in nature from current explanation methods, such as Grad-CAM. Perception visualization provides a visual representation of what the DNN perceives in the input image by depicting what visual patterns the latent representation corresponds to. Results of our user study demonstrate that humans can better understand and predict the system's decisions when perception visualizations are available.
  arXiv  Detail & Related papers  (2022-04-21T07:18:55Z)
• PQA: Perceptual Question Answering [35.051664704756995]
  Perceptual organization remains one of the very few established theories on the human visual system. In this paper, we rejuvenate the study of perceptual organization, by advocating two positional changes. We examine purposefully generated synthetic data, instead of complex real imagery. We then borrow insights from human psychology to design an agent that casts perceptual organization as a self-attention problem.
  arXiv  Detail & Related papers  (2021-04-08T08:06:21Z)
• Predictive coding feedback results in perceived illusory contours in a recurrent neural network [0.0]
  We equip a deep feedforward convolutional network with brain-inspired recurrent dynamics. We show that the perception of illusory contours could involve feedback connections.
  arXiv  Detail & Related papers  (2021-02-03T09:07:09Z)
• What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space [88.37185513453758]
  We propose a method to visualize and understand the class-wise knowledge learned by deep neural networks (DNNs) under different settings. Our method searches for a single predictive pattern in the pixel space to represent the knowledge learned by the model for each class. In the adversarial setting, we show that adversarially trained models tend to learn more simplified shape patterns.
  arXiv  Detail & Related papers  (2021-01-18T06:38:41Z)
• What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions [50.435861435121915]
  We use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. Our experiments show that our "muscly-supervised" representation outperforms a visual-only state-of-the-art method MoCo.
  arXiv  Detail & Related papers  (2020-10-16T17:46:53Z)
• A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction [68.76594695163386]
  We propose a Deep Drift-Diffusion model inspired by psychologists to predict aesthetic score distribution from images. The DDD model can describe the psychological process of aesthetic perception instead of traditional modeling of the results of assessment. Our novel DDD model is simple but efficient, which outperforms the state-of-the-art methods in aesthetic score distribution prediction.
  arXiv  Detail & Related papers  (2020-10-15T11:01:46Z)
• Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape bias [18.003188982585737]
  Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme results in models employing Convolutional Neural Networks (CNNs) It is believed that the cost function forces the CNN to take a greedy approach and develop a proclivity for local information like texture to increase accuracy, thus failing to explore any global statistics. We propose CognitiveCNN, a new intuitive architecture, inspired from feature integration theory in psychology to utilise human interpretable feature like shape, texture, edges etc. to reconstruct, and classify the image.
  arXiv  Detail & Related papers  (2020-06-25T22:32:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.