Advances in Weakly Supervised Object Detection: Leveraging Unlabeled Data for Enhanced Performance

Abstract

Weakly supervised object detection represents a burgeoning field within the realm of computer vision, reflecting the growing interest in developing models that can effectively identify and classify objects with minimal labeled data. This paper offers a comprehensive classification of contemporary, state-of-the-art deep learning models tailored for weakly supervised target detection. The classification encompasses four principal categories: Multi-Instance Learning (MIL), Class Activation Mapping (CAM), Deep Weakly Supervised Learning leveraging Attention Mechanisms, and Weakly Supervised Object Detection employing Pseudo-labels. Each category represents a unique approach to the challenge of discerning and localizing objects with limited supervision, emphasizing different aspects of learning from sparse or imprecise annotations. Our analysis delves into the intricate methodologies and theoretical foundations underlying these models, offering insights into their practical applications and performance metrics. Furthermore, we explore the evolutionary trajectory of these techniques, highlighting their advancements and the pivotal role they play in advancing the frontiers of automated object detection in diverse and complex environments. This synthesis not only charts the current landscape of weakly supervised object detection but also paves the way for future research directions in this dynamic and rapidly evolving field.