Relation Networks for Object Detection
This work addresses the long-standing problem of effectively modeling relations between objects for object detection, which could benefit computer vision researchers and practitioners.
This paper introduces an object relation module that processes object sets simultaneously, modeling interactions between their appearance and geometry. This module improves object recognition and duplicate removal in object detection pipelines, leading to the first fully end-to-end object detector.
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.