ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)

• URL: http://arxiv.org/abs/2412.17984v1
• Date: Mon, 23 Dec 2024 21:06:08 GMT
• Title: ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)
• Authors: Sofia Marino, Jennifer Vandoni, Emanuel Aldea, Ichraq Lemghari, Sylvie Le Hégarat-Mascle, Frédéric Jurie,
• Abstract summary: We present the general context of the tasks we proposed to the community, we introduce the data that were prepared for the competition and we provide a summary of the results along with a description of the top three winning entries. The competition was centered around domain adaptation and generalization, and our core aim is to foster interest and facilitate advancement on these topics by providing a high-quality, lightweight, real world dataset able to support fast prototyping and validation of novel ideas.
• Score: 3.1353272648618358
• License:
• Abstract: In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we present the general context of the tasks we proposed to the community, we introduce the data that were prepared for the competition and we provide a summary of the results along with a description of the top three winning entries. The competition was centered around domain adaptation and generalization, and our core aim is to foster interest and facilitate advancement on these topics by providing a high-quality, lightweight, real world dataset able to support fast prototyping and validation of novel ideas.

Related papers

• Auto-survey Challenge [0.0]
  We present a novel platform for evaluating the capability of Large Language Models (LLMs) to autonomously compose and critique survey papers. Within this framework, we organized a competition for the AutoML conference 2023. Entrants are tasked with presenting stand-alone models adept at authoring articles from designated prompts and subsequently appraising them.
  arXiv  Detail & Related papers  (2023-10-06T09:12:35Z)
• The Robust Semantic Segmentation UNCV2023 Challenge Results [99.97867942388486]
  This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural adversarial scenarios. The report presents the results of 19 submitted entries, with numerous techniques drawing inspiration from cutting-edge uncertainty quantification methodologies.
  arXiv  Detail & Related papers  (2023-09-27T08:20:03Z)
• EFaR 2023: Efficient Face Recognition Competition [51.77649060180531]
  The paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023) The competition received 17 submissions from 6 different teams. The submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.
  arXiv  Detail & Related papers  (2023-08-08T09:58:22Z)
• ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images [198.35937007558078]
  The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios.
  arXiv  Detail & Related papers  (2023-06-05T22:20:52Z)
• ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents [3.6700088931938835]
  ICDAR has a long tradition in hosting competitions to benchmark the state-of-the-art. To raise the bar over previous competitions, we engineered a hard competition dataset and proposed the recent DocLayNet dataset for training. We recognize interesting combinations of recent computer vision models, data augmentation strategies and ensemble methods to achieve remarkable accuracy in the task we posed.
  arXiv  Detail & Related papers  (2023-05-24T09:56:47Z)
• ICDAR 2023 Competition on Hierarchical Text Detection and Recognition [60.68100769639923]
  The competition is aimed to promote research into deep learning models and systems that can jointly perform text detection and recognition. We present details of the proposed competition organization, including tasks, datasets, evaluations, and schedule. During the competition period (from January 2nd 2023 to April 1st 2023), at least 50 submissions from more than 20 teams were made in the 2 proposed tasks.
  arXiv  Detail & Related papers  (2023-05-16T18:56:12Z)
• ICDAR 2023 Competition on Reading the Seal Title [58.866588777012744]
  To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST) We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text and text contents. The competition attracted 53 participants from academia and industry including 28 submissions for Task 1 and 25 submissions for Task 2, which demonstrated significant interest in this challenging task.
  arXiv  Detail & Related papers  (2023-04-24T10:01:41Z)
• The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results [1.4824891788575418]
  This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021) The goal of the competition is to provide an objective comparison of the state-of-the-art methods in neural network verification. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this competition.
  arXiv  Detail & Related papers  (2021-08-31T01:29:56Z)
• Analysing Affective Behavior in the second ABAW2 Competition [70.86998050535944]
  The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second -- following the first very successful ABAW Competition held in conjunction with IEEE FG 2020- Competition that aims at automatically analyzing affect.
  arXiv  Detail & Related papers  (2021-06-14T11:30:19Z)
• Towards robust and domain agnostic reinforcement learning competitions [12.731614722371376]
  Reinforcement learning competitions have formed the basis for standard research benchmarks. Despite this, a majority of challenges suffer from the same fundamental problems. We present a new framework of competition design that promotes the development of algorithms that overcome these barriers.
  arXiv  Detail & Related papers  (2021-06-07T16:15:46Z)

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.