Semiotics Networks Representing Perceptual Inference

• URL: http://arxiv.org/abs/2310.05212v4
• Date: Mon, 1 Jul 2024 20:23:31 GMT
• Title: Semiotics Networks Representing Perceptual Inference
• Authors: David Kupeev, Eyal Nitcany,
• Abstract summary: We present a computational model designed to track and simulate the perception of objects. Our model is not limited to persons and can be applied to any system featuring a loop involving the processing from "internal" to "external" representations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Every day, humans perceive objects and communicate these perceptions through various channels. In this paper, we present a computational model designed to track and simulate the perception of objects, as well as their representations as conveyed in communication. We delineate two fundamental components of our internal representation, termed "observed" and "seen", which we correlate with established concepts in computer vision, namely encoding and decoding. These components are integrated into semiotic networks, which simulate perceptual inference of object perception and human communication. Our model of object perception by a person allows us to define object perception by {\em a network}. We demonstrate this with an example of an image baseline classifier by constructing a new network that includes the baseline classifier and an additional layer. This layer produces the images "perceived" by the entire network, transforming it into a perceptualized image classifier. This facilitates visualization of the acquired network. Within our network, the image representations become more efficient for classification tasks when they are assembled and randomized. In our experiments, the perceptualized network outperformed the baseline classifier on MNIST training databases consisting of a restricted number of images. Our model is not limited to persons and can be applied to any system featuring a loop involving the processing from "internal" to "external" representations.

Related papers

• Connectivity-Inspired Network for Context-Aware Recognition [1.049712834719005]
  We focus on the effect of incorporating circuit motifs found in biological brains to address visual recognition. Our convolutional architecture is inspired by the connectivity of human cortical and subcortical streams. We present a new plug-and-play module to model context awareness.
  arXiv  Detail & Related papers  (2024-09-06T15:42:10Z)
• Understanding the Role of Pathways in a Deep Neural Network [4.456675543894722]
  We analyze a convolutional neural network (CNN) trained in the classification task and present an algorithm to extract the diffusion pathways of individual pixels. We find that the few largest pathways of an individual pixel from an image tend to cross the feature maps in each layer that is important for classification.
  arXiv  Detail & Related papers  (2024-02-28T07:53:19Z)
• Finding Concept Representations in Neural Networks with Self-Organizing Maps [2.817412580574242]
  We show how self-organizing maps can be used to inspect how activation of layers of neural networks correspond to neural representations of abstract concepts. We show that, among the measures tested, the relative entropy of the activation map for a concept is a suitable candidate and can be used as part of a methodology to identify and locate the neural representation of a concept.
  arXiv  Detail & Related papers  (2023-12-10T12:10:34Z)
• Image segmentation with traveling waves in an exactly solvable recurrent neural network [71.74150501418039]
  We show that a recurrent neural network can effectively divide an image into groups according to a scene's structural characteristics. We present a precise description of the mechanism underlying object segmentation in this network. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images.
  arXiv  Detail & Related papers  (2023-11-28T16:46:44Z)
• CLiC: Concept Learning in Context [54.81654147248919]
  This paper builds upon recent advancements in visual concept learning. It involves acquiring a visual concept from a source image and subsequently applying it to an object in a target image. To localize the concept learning, we employ soft masks that contain both the concept within the mask and the surrounding image area.
  arXiv  Detail & Related papers  (2023-11-28T01:33:18Z)
• Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition [77.95361323613147]
  Current state-of-the-art visual recognition systems rely on pretraining a neural network on a large-scale dataset and finetuning the network weights on a smaller dataset. In this work, we prove that dynamically adapting network architectures tailored for each domain task along with weight finetuning benefits in both efficiency and effectiveness. Our method can be easily generalized to an unsupervised paradigm by replacing supernet training with self-supervised learning in the source domain tasks and performing linear evaluation in the downstream tasks.
  arXiv  Detail & Related papers  (2021-03-31T08:15:17Z)
• Understanding the Role of Individual Units in a Deep Neural Network [85.23117441162772]
  We present an analytic framework to systematically identify hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.
  arXiv  Detail & Related papers  (2020-09-10T17:59:10Z)
• Visual Concept Reasoning Networks [93.99840807973546]
  A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. We propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. Our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.
  arXiv  Detail & Related papers  (2020-08-26T20:02:40Z)
• Disentangle Perceptual Learning through Online Contrastive Learning [16.534353501066203]
  Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. In this paper, we argue that, among the features representation from the pre-trained classification network, only limited dimensions are related to human visual perception. Under such an assumption, we try to disentangle the perception-relevant dimensions from the representation through our proposed online contrastive learning.
  arXiv  Detail & Related papers  (2020-06-24T06:48:38Z)
• Learning Representations by Predicting Bags of Visual Words [55.332200948110895]
  Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions.
  arXiv  Detail & Related papers  (2020-02-27T16:45:25Z)

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.