Visual relationship detection with object spatial distribution

Recently, object recognition techniques have been rapidly developed. Most of existing object recognition focused on recognizing several independent concepts. The relationship of objects is also an important problem, which shows in-depth semantic information of images. In this work, toward general visual relationship detection, we propose a method to integrate spatial distribution of object to facilitate visual relation detection. Spatial distribution can not only reflect positional relation of object but also describe structural information between objects. Spatial distributions are described with different features such as positional relation, size relation, shape relation, and so on. By combing spatial distribution features with visual and concept features, we establish a modeling method to make these three aspects working together to facilitate visual relationship detection. To evaluate the proposed method, we conduct experiments on two datasets, which are the Stanford VRD dataset, and a newly proposed larger new dataset which contains 15k images. Experimental results demonstrate that our approach is effective.