Quantitative analysis of visual representation of sign elements in
COVID-19 context
- URL: http://arxiv.org/abs/2112.08219v1
- Date: Wed, 15 Dec 2021 15:54:53 GMT
- Title: Quantitative analysis of visual representation of sign elements in
COVID-19 context
- Authors: Mar\'ia Jes\'us Cano-Mart\'inez and Miguel Carrasco and Joaqu\'in
Sandoval and C\'esar Gonz\'alez-Mart\'in
- Abstract summary: We propose using computer analysis to perform a quantitative analysis of the elements used in the visual creations produced in reference to the epidemic.
The images compiled in The Covid Art Museum's Instagram account to analyze the different elements used to represent subjective experiences with regard to a global event.
This research reveals that the elements that are repeated in images to create narratives and the relations of association that are established in the sample.
- Score: 2.9409535911474967
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Representation is the way in which human beings re-present the reality of
what is happening, both externally and internally. Thus, visual representation
as a means of communication uses elements to build a narrative, just as spoken
and written language do. We propose using computer analysis to perform a
quantitative analysis of the elements used in the visual creations that have
been produced in reference to the epidemic, using the images compiled in The
Covid Art Museum's Instagram account to analyze the different elements used to
represent subjective experiences with regard to a global event. This process
has been carried out with techniques based on machine learning to detect
objects in the images so that the algorithm can be capable of learning and
detecting the objects contained in each study image. This research reveals that
the elements that are repeated in images to create narratives and the relations
of association that are established in the sample, concluding that, despite the
subjectivity that all creation entails, there are certain parameters of shared
and reduced decisions when it comes to selecting objects to be included in
visual representations
Related papers
- An Image-based Typology for Visualization [23.716718517642878]
We present and discuss the results of a qualitative analysis of visual representations from images.
We derive a typology of 10 visualization types of defined groups.
We provide a dataset of 6,833 tagged images and an online tool that can be used to explore and analyze the large set of labeled images.
arXiv Detail & Related papers (2024-03-07T04:33:42Z) - Leveraging Open-Vocabulary Diffusion to Camouflaged Instance
Segmentation [59.78520153338878]
Text-to-image diffusion techniques have shown exceptional capability of producing high-quality images from text descriptions.
We propose a method built upon a state-of-the-art diffusion model, empowered by open-vocabulary to learn multi-scale textual-visual features for camouflaged object representations.
arXiv Detail & Related papers (2023-12-29T07:59:07Z) - Exploring Affordance and Situated Meaning in Image Captions: A
Multimodal Analysis [1.124958340749622]
We annotate images from the Flickr30k dataset with five perceptual properties: Affordance, Perceptual Salience, Object Number, Cue Gazeing, and Ecological Niche Association (ENA)
Our findings reveal that images with Gibsonian affordance show a higher frequency of captions containing 'holding-verbs' and 'container-nouns' compared to images displaying telic affordance.
arXiv Detail & Related papers (2023-05-24T01:30:50Z) - Compositional Scene Modeling with Global Object-Centric Representations [44.43366905943199]
Humans can easily identify the same object, even if occlusions exist, by completing the occluded parts based on its canonical image in the memory.
This paper proposes a compositional scene modeling method to infer global representations of canonical images of objects without any supervision.
arXiv Detail & Related papers (2022-11-21T14:36:36Z) - Context-driven Visual Object Recognition based on Knowledge Graphs [0.8701566919381223]
We propose an approach that enhances deep learning methods by using external contextual knowledge encoded in a knowledge graph.
We conduct a series of experiments to investigate the impact of different contextual views on the learned object representations for the same image dataset.
arXiv Detail & Related papers (2022-10-20T13:09:00Z) - Perceptual Grouping in Contrastive Vision-Language Models [59.1542019031645]
We show how vision-language models are able to understand where objects reside within an image and group together visually related parts of the imagery.
We propose a minimal set of modifications that results in models that uniquely learn both semantic and spatial information.
arXiv Detail & Related papers (2022-10-18T17:01:35Z) - Compositional Mixture Representations for Vision and Text [43.2292923754127]
A common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning.
We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision.
arXiv Detail & Related papers (2022-06-13T18:16:40Z) - Integrating Visuospatial, Linguistic and Commonsense Structure into
Story Visualization [81.26077816854449]
We first explore the use of constituency parse trees for encoding structured input.
Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story.
Third, we incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images.
arXiv Detail & Related papers (2021-10-21T00:16:02Z) - Understanding Synonymous Referring Expressions via Contrastive Features [105.36814858748285]
We develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels.
We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets.
arXiv Detail & Related papers (2021-04-20T17:56:24Z) - Learning Object Detection from Captions via Textual Scene Attributes [70.90708863394902]
We argue that captions contain much richer information about the image, including attributes of objects and their relations.
We present a method that uses the attributes in this "textual scene graph" to train object detectors.
We empirically demonstrate that the resulting model achieves state-of-the-art results on several challenging object detection datasets.
arXiv Detail & Related papers (2020-09-30T10:59:20Z) - Probing Contextual Language Models for Common Ground with Visual
Representations [76.05769268286038]
We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations.
Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories.
Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans.
arXiv Detail & Related papers (2020-05-01T21:28:28Z)
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.