![[2602.16322] A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16322] A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks
Summary
This paper presents a self-supervised learning approach to enhance feature representations in object detection tasks, reducing the need for labeled data.
Why It Matters
The research addresses a critical challenge in AI and computer vision: the scarcity of labeled data for training models. By demonstrating that self-supervised learning can improve feature extraction, this work has implications for reducing costs and resources in developing object detection applications.
Key Takeaways
- Self-supervised learning can enhance feature extractors for object detection.
- The proposed model outperforms traditional methods pre-trained on ImageNet.
- Less reliance on labeled data can significantly reduce training costs.
- Improved feature representations lead to more reliable and robust models.
- This approach could accelerate advancements in AI applications requiring object detection.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.16322 (cs) [Submitted on 18 Feb 2026] Title:A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks Authors:Santiago C. Vilabella, Pablo Pérez-Núñez, Beatriz Remeseiro View a PDF of the paper titled A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks, by Santiago C. Vilabella and 2 other authors View PDF HTML (experimental) Abstract: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 models has become a significant challenge. Addressing complex problems like object detection demands considerable time and resources for data labeling to achieve meaningful results. For companies developing such applications, this entails extensive investment in highly skilled personnel or costly outsourcing. This research work aims to demonstrate that enhancing feature extractors can substantially alleviate this challenge, enabling models to learn more effective representations with less labeled data. Utilizing a self-supervised learning strategy, we present a model trained on unlabeled data that outperforms state-of-the-art feature extractors pre-trained on ImageNet and particularly designed for object detection tasks. Moreover, the results demonstrate that our approach encourages the model to focus on the most relevant aspects o...
