Improving object detection with relation graph inference

Abstract

Many classic object detection approaches have proven that detection performance can be improved by adding the object's context information. However, only a few methods have attempted to exploit the object-to-object relationship during learning. The reason for this is that objects may appear at different locations in an image, with an arbitrary size and scale. This makes it difficult to model the objects in a unified way within a network. Inspired by Graph Convolutional Network (GCN), we propose a detection algorithm that can infer the relationship among multiple objects during the inference, achieved by constructing a relation graph dynamically with a self-adopted attention mechanism. The relation graph encodes both the geometric and visual relationship between objects. This can enrich the object feature by aggregating the information from the object and its relevant neighbors. Experiments show that our proposed module can efficiently improve the detection performance of existing object detectors.