An Approach to Incorporating Implicit Knowledge in Object Detection Models

Abstract

Current object detection methods typically focus on addressing the distribution discrepancies between source and target domains. However, solely concentrating on this aspect may lead to overlooking the inherent limitations of the samples themselves. This study proposes a method to integrate implicit knowledge into object detection models, aiming to enhance the models’ effectiveness in identifying target features within images. We analyze the sources of information loss in object detection models, treating this loss as a form of implicit knowledge and modeling it in the form of dictionaries. We explore potentially effective ways of integrating latent knowledge into the models and then apply it to object detection models. The models demonstrate a 1% and 0.8% improvement in mean average precision(mAP) in the UA-DETRAC and KITTI datasets, respectively. The results indicate that the proposed method can effectively enhance the relevant metrics of object detection models without significantly increasing the parameter or computational overhead. By excavating and utilizing implicit knowledge, we enhance the performance and efficiency of the models, offering new perspectives and methods for addressing challenges in practical applications.

Keywords

• multi-task
• implicit knowledge
• dictionary parameters
• object detection