(Re)framing Built Heritage through the Machinic Gaze
- URL: http://arxiv.org/abs/2310.04628v1
- Date: Fri, 6 Oct 2023 23:48:01 GMT
- Title: (Re)framing Built Heritage through the Machinic Gaze
- Authors: Vanicka Arora, Liam Magee, Luke Munn
- Abstract summary: We argue that the proliferation of machine learning and vision technologies create new scopic regimes for heritage.
We introduce the term machinic gaze' to conceptualise the reconfiguration of heritage representation via AI models.
- Score: 3.683202928838613
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Built heritage has been both subject and product of a gaze that has been
sustained through moments of colonial fixation on ruins and monuments,
technocratic examination and representation, and fetishisation by aglobal
tourist industry. We argue that the recent proliferation of machine learning
and vision technologies create new scopic regimes for heritage: storing and
retrieving existing images from vast digital archives, and further imparting
their own distortions upon its visual representation. We introduce the term
`machinic gaze' to conceptualise the reconfiguration of heritage representation
via AI models. To explore how this gaze reframes heritage, we deploy an
image-text-image pipeline that reads, interprets, and resynthesizes images of
several UNESCO World Heritage Sites. Employing two concepts from media studies
-- heteroscopia and anamorphosis -- we describe the reoriented perspective that
machine vision systems introduce. We propose that the machinic gaze highlights
the artifice of the human gaze and its underlying assumptions and practices
that combine to form established notions of heritage.
Related papers
- KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities [93.74881034001312]
We conduct a systematic study on the fidelity of entities in text-to-image generation models.
We focus on their ability to generate a wide range of real-world visual entities, such as landmark buildings, aircraft, plants, and animals.
Our findings reveal that even the most advanced text-to-image models often fail to generate entities with accurate visual details.
arXiv Detail & Related papers (2024-10-15T17:50:37Z) - When Does Perceptual Alignment Benefit Vision Representations? [76.32336818860965]
We investigate how aligning vision model representations to human perceptual judgments impacts their usability.
We find that aligning models to perceptual judgments yields representations that improve upon the original backbones across many downstream tasks.
Our results suggest that injecting an inductive bias about human perceptual knowledge into vision models can contribute to better representations.
arXiv Detail & Related papers (2024-10-14T17:59:58Z) - Attention is All You Want: Machinic Gaze and the Anthropocene [2.4554686192257424]
computational vision interprets and synthesises representations of the Anthropocene.
We examine how this emergent machinic gaze both looks out, through its compositions of futuristic landscapes, and looks back, towards an observing and observed human subject.
In its varied assistive, surveillant and generative roles, computational vision not only mirrors human desire but articulates oblique demands of its own.
arXiv Detail & Related papers (2024-05-16T00:00:53Z) - Restoring Ancient Ideograph: A Multimodal Multitask Neural Network
Approach [11.263700269889654]
This paper proposes a novel Multimodal Multitask Restoring Model (MMRM) to restore ancient texts.
It combines context understanding with residual visual information from damaged ancient artefacts, enabling it to predict damaged characters and generate restored images simultaneously.
arXiv Detail & Related papers (2024-03-11T12:57:28Z) - From Pampas to Pixels: Fine-Tuning Diffusion Models for Ga\'ucho
Heritage [0.0]
This paper addresses the potential of Latent Diffusion Models (LDMs) in representing local cultural concepts, historical figures, and endangered species.
Our objective is to contribute to the broader understanding of how generative models can help to capture and preserve the cultural and historical identity of regions.
arXiv Detail & Related papers (2024-01-10T19:34:52Z) - Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and
Multi-Source Supervision [5.517240672957627]
We propose a novel knowledge-aware artifact image synthesis approach that brings lost historical objects accurately into their visual forms.
Compared to existing approaches, our proposed model produces higher-quality artifact images that align better with the implicit details and historical knowledge contained within written documents.
arXiv Detail & Related papers (2023-12-13T11:03:07Z) - Unsupervised Compositional Concepts Discovery with Text-to-Image
Generative Models [80.75258849913574]
In this paper, we consider the inverse problem -- given a collection of different images, can we discover the generative concepts that represent each image?
We present an unsupervised approach to discover generative concepts from a collection of images, disentangling different art styles in paintings, objects, and lighting from kitchen scenes, and discovering image classes given ImageNet images.
arXiv Detail & Related papers (2023-06-08T17:02:15Z) - 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) - Automatic Modeling of Social Concepts Evoked by Art Images as Multimodal
Frames [1.4502611532302037]
Social concepts referring to non-physical objects are powerful tools to describe, index, and query the content of visual data.
We propose a software approach to represent social concepts as multimodal frames, by integrating multisensory data.
Our method focuses on the extraction, analysis, and integration of multimodal features from visual art material tagged with the concepts of interest.
arXiv Detail & Related papers (2021-10-14T14:50:22Z) - Enhancing Photorealism Enhancement [83.88433283714461]
We present an approach to enhancing the realism of synthetic images using a convolutional network.
We analyze scene layout distributions in commonly used datasets and find that they differ in important ways.
We report substantial gains in stability and realism in comparison to recent image-to-image translation methods.
arXiv Detail & Related papers (2021-05-10T19:00:49Z) - Learning Patterns of Tourist Movement and Photography from Geotagged
Photos at Archaeological Heritage Sites in Cuzco, Peru [73.52315464582637]
We build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit in Cuzco, Peru.
Our goals are to (1) understand how the intensification of tourism intersects with heritage regulations and social media, aiding in the articulation of travel patterns across Cuzco's heritage landscape; and to (2) assess how aesthetic preferences and visuality become entangled with the rapidly evolving expectations of tourists, whose travel narratives are curated on social media and grounded in historic site representations.
arXiv Detail & Related papers (2020-06-29T22:49:59Z)
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.