LGA-RCNN: Loss-Guided Attention for Object Detection
This work addresses object detection problems for computer vision applications, but it appears incremental as it builds on existing RCNN methods with a novel attention module.
The paper tackled performance issues in object detection under challenges like camouflage and blur by proposing LGA-RCNN, which uses a loss-guided attention module to highlight object regions and fuse them with global information, resulting in improved detection accuracy.
Object detection is widely studied in computer vision filed. In recent years, certain representative deep learning based detection methods along with solid benchmarks are proposed, which boosts the development of related researchs. However, existing detection methods still suffer from undesirable performance under challenges such as camouflage, blur, inter-class similarity, intra-class variance and complex environment. To address this issue, we propose LGA-RCNN which utilizes a loss-guided attention (LGA) module to highlight representative region of objects. Then, those highlighted local information are fused with global information for precise classification and localization.