Contextual Matrix for Reasoning-based Object Detection
It has been well recognized that contextual information or relation between objects helps object recognition.However,the most of current state-of-the-art object detectors rely on recognizing object instances individually and lack contextual infor-mation.To make full use of the contextual information,we proposed a specific contextual matrix to enhance the context infor-mation reasoning ability of the detection model by incorporating the contextual matrix in object detection.Specifically,the con-text matrix has three forms which obtained from the annotations of different data sets,to enhance the context information through the relationship,attributes and the co-occurrence probability of the objects.Further,the contextual matrix reasoning-based model was lightweight and flexible enough to enhance different object detection baseline models and help few-shot task.Extensive experiments illustrate that the proposed contextual matrix reasoning-based detector can consistently improve various detectors on different benchmarks.
deep learningobject detectioncontextual matrixvisual reasoning
NETL
NSTL
万方数据
