OICR-APIM: weakly supervised object detection with integrated global attention and pseudo-instance mining
Jiayi Liu, Ge Song, Xizi Li, Hua Li · 2024
In the absence of instance-level supervision information in weakly supervised object detection, there are currently two primary challenges. The first challenge is the difficulty in clearly distinguishing multiple target instances, leading to issues of missed detection and confusion. Another issue is that during the training process, it is easy to be trapped in local optimal solutions, making it difficult to comprehensively detect target instances. To address the above problems, a weakly supervised object detection algorithm is proposed that combines attention mechanism and multi-instance mining. The algorithm adds incorporates an attention module enabling the detector to detect the full view of the target and alleviate local optima issues. Additionally, a pseudo-Instance mining algorithm is designed specifically for multi-instance detection, to discover instances overlooked by the detector, reducing instance omissions and thereby enhancing detection performance. The algorithm was evaluated using the PASCAL VOC2007 and VOC2012 datasets, demonstrating that it can detect more instances compared to other weakly supervised algorithms, effectively enhancing weakly supervised detection accuracy.