Enhanced Attention Guided Teacher–Student Network for Weakly Supervised Object Detection

Abstract

Weakly Supervised Object Detection (WSOD) has attracted increasing attention due to the convenience and low-cost of acquiring image-level annotations. Most existing WSOD methods follow Multiple Instance Learning (MIL) paradigm to select bounding boxes from proposals based on their classification scores. However, MIL-based WSOD methods often focus on discriminative regions, leading to incomplete and missing instances. To address these issues, we introduce a novel WSOD framework named Enhanced Attention Guided Teacher–Student Network (EATSN). This framework aims to improve detection performance through the guidance of attention map and consistency learning. specifically, we initially train a teacher network using MIL process to generate pseudo ground-truth labels. Subsequently, the weak augmented and strong augmented images are fed into teacher and student models to produce the enhanced attention map. During the training iterations, pseudo labels are utilized to guide the student model, while the teacher model refines its parameters through the Exponential Moving Average(EMA) from the student. Finally we design a proposal selection method that leverages the enhanced attention map and bounding boxes scores to achieve better detection results. Experimental results on benchmark datasets demonstrate that our method achieves comparable performance.