Improving Visual Relationship Detection With Two-Stage Correlation Exploitation

Visual relationship detection, as a challenging task used to find and distinguish interactions between object-pairs in one image, has received much attention recently. In this work, we devise a unified visual relationship detection framework with two types of correlation exploitation to address the combination explosion problem in the object-pairs proposing stage and the non-exclusive label problem in the predicate recognition stage. In the object-pairs proposing stage, with the exploitation of relative location correlation between two objects in one pair, one location-embedded rating module (LRM) is developed to effectively select plausible proposals. In the predicate recognition stage, one label-correlation graph module (LGM) is introduced to measure the implicit semantic correlation among predicates; and then assign discrete distributed labels to predicates to improve the precision of top-n recall. Experiments on the two widely used VRD and VG datasets show that our proposed method outperforms current state-of-the-art methods.

Automated summary

Visual relationship detection is a challenging task that has gained much attention recently. The proposed unified framework successfully addresses the combination and label problems in object-pairs proposing and predicate recognition stages. Experimental results show that this method outperforms current state-of-the-art methods on widely used datasets.