ArcAid: Analysis of Archaeological Artifacts using Drawings

• URL: http://arxiv.org/abs/2211.09480v3
• Date: Sat, 20 Jan 2024 16:54:52 GMT
• Title: ArcAid: Analysis of Archaeological Artifacts using Drawings
• Authors: Offry Hayon, Stefan M\"unger, Ilan Shimshoni, Ayellet Tal
• Abstract summary: Archaeology is an intriguing domain for computer vision. It suffers not only from shortage in (labeled) data, but also from highly-challenging data, which is often extremely abraded and damaged. This paper proposes a novel semi-supervised model for classification and retrieval of images of archaeological artifacts.
• Score: 23.906975910478142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Archaeology is an intriguing domain for computer vision. It suffers not only from shortage in (labeled) data, but also from highly-challenging data, which is often extremely abraded and damaged. This paper proposes a novel semi-supervised model for classification and retrieval of images of archaeological artifacts. This model utilizes unique data that exists in the domain -- manual drawings made by special artists. These are used during training to implicitly transfer the domain knowledge from the drawings to their corresponding images, improving their classification results. We show that while learning how to classify, our model also learns how to generate drawings of the artifacts, an important documentation task, which is currently performed manually. Last but not least, we collected a new dataset of stamp-seals of the Southern Levant. Our code and dataset are publicly available.

Related papers

• HaSPeR: An Image Repository for Hand Shadow Puppet Recognition [2.048226951354646]
  Hand shadow puppetry, also known as shadowgraphy or ombromanie, is a form of theatrical art and storytelling. We present a novel dataset consisting of 8,340 images of hand shadow puppets across 11 classes extracted from both professional and amateur hand shadow puppeteer clips. Our findings show a substantial performance superiority of traditional convolutional models over attention-based transformer architectures.
  arXiv  Detail & Related papers  (2024-08-19T18:56:24Z)
• Measuring Style Similarity in Diffusion Models [118.22433042873136]
  We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image. We also propose a method to extract style attribute descriptors that can be used to style of a generated image to the images used in the training dataset of a text-to-image model.
  arXiv  Detail & Related papers  (2024-04-01T17:58:30Z)
• 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)
• AutArch: An AI-assisted workflow for object detection and automated recording in archaeological catalogues [37.69303106863453]
  This paper introduces a new workflow for collecting data from archaeological find catalogues available as legacy resources. The workflow relies on custom software (AutArch) supporting image processing, object detection, and interactive means of validating and adjusting automatically retrieved data. We integrate artificial intelligence (AI) in terms of neural networks for object detection and classification into the workflow.
  arXiv  Detail & Related papers  (2023-11-29T17:24:04Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• Augraphy: A Data Augmentation Library for Document Images [59.457999432618614]
  Augraphy is a Python library for constructing data augmentation pipelines. It provides strategies to produce augmented versions of clean document images that appear to have been altered by standard office operations.
  arXiv  Detail & Related papers  (2022-08-30T22:36:19Z)
• Learning Co-segmentation by Segment Swapping for Retrieval and Discovery [67.6609943904996]
  The goal of this work is to efficiently identify visually similar patterns from a pair of images. We generate synthetic training pairs by selecting object segments in an image and copy-pasting them into another image. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset.
  arXiv  Detail & Related papers  (2021-10-29T16:51:16Z)
• Transfer Learning for Pose Estimation of Illustrated Characters [37.04208600867858]
  A pose estimator for the illustrated character domain would provide a valuable prior for assistive content creation tasks. We bridge this domain gap by efficiently transfer-learning from both domain-specific and task-specific source models. We apply the resultant state-of-the-art character pose estimator to solve the novel task of pose-guided illustration retrieval.
  arXiv  Detail & Related papers  (2021-08-04T02:37:28Z)
• Iconographic Image Captioning for Artworks [2.3859169601259342]
  This work utilizes a novel large-scale dataset of artwork images annotated with concepts from the Iconclass classification system designed for art and iconography. The annotations are processed into clean textual description to create a dataset suitable for training a deep neural network model on the image captioning task. A transformer-based vision-language pre-trained model is fine-tuned using the artwork image dataset. The quality of the generated captions and the model's capacity to generalize to new data is explored by employing the model on a new collection of paintings and performing an analysis of the relation between commonly generated captions and the artistic genre.
  arXiv  Detail & Related papers  (2021-02-07T23:11:33Z)
• Multi-Class Zero-Shot Learning for Artistic Material Recognition [68.8204255655161]
  Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no labelled examples of the data to be classified are provided during the training stage. Here we outline a model to identify the materials with which a work of art was created, by learning the relationship between English descriptions of the subject of a piece and its composite materials. We produce a model which is capable of correctly identifying the materials used on pieces from an entirely distinct museum dataset.
  arXiv  Detail & Related papers  (2020-10-26T19:04:50Z)
• Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
  Current datasets only tell you where people are, not where they could be. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
  arXiv  Detail & Related papers  (2020-08-19T23:19:08Z)

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.