A Self-Supervised Approach for Enhanced Feature Representations in Object Detection Tasks
This work addresses the problem of high labeling costs for companies developing object detection applications, offering an incremental improvement in feature representation.
The paper tackles the challenge of limited labeled data for object detection by using self-supervised learning to enhance feature extractors, resulting in a model that outperforms state-of-the-art pre-trained feature extractors on object detection tasks.
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 of an object, thus achieving better feature representations and, therefore, reinforcing its reliability and robustness.