A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks

  1. Vilabella, Santiago C. 2
  2. Pérez-Núñez, Pablo 1
  3. Remeseiro, Beatriz 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Universidad Internacional Menéndez Pelayo
    info

    Universidad Internacional Menéndez Pelayo

    Madrid, España

    ROR https://ror.org/02zngfv65

Actes de conférence:
IJCNN 2024 Conferences Proceedings

ISSN: 2161-4407,  2161-4393

ISBN: 979-8-3503-5931-2,  979-8-3503-5932-9

Année de publication: 2024

Volumen: 2

Pages: 1-8

Congreso: 2024 International Joint Conference on Neural Networks (IJCNN)

Type: Communication dans un congrès

DOI: 10.1109/IJCNN60899.2024.10651388 SCOPUS: 2-s2.0-85204973206 GOOGLE SCHOLAR

Résumé

In the fast-evolving field of artificial intelligence,where models are increasingly growing in complexity and size,the availability of labeled data for training deep learning modelshas become a significant challenge. Addressing complex problemslike object detection demands considerable time and resourcesfor data labeling to achieve meaningful results. For companiesdeveloping such applications, this entails extensive investmentin highly skilled personnel or costly outsourcing. This researchwork aims to demonstrate that enhancing feature extractors cansubstantially alleviate this challenge, enabling models to learnmore effective representations with less labeled data. Utilizinga self-supervised learning strategy, we present a model trainedon unlabeled data that outperforms state-of-the-art feature extractors pre-trained on ImageNet and particularly designed forobject detection tasks. Moreover, the results demonstrate thatour approach encourages the model to focus on the most relevantaspects of an object, thus achieving better feature representationsand, therefore, reinforcing its reliability and robustness.

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Information sur le financement

Grant PID2019-109238GB-C21 funded by MICIU/AEI/10.13039/501100011033.

Financeurs

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