Scene graph generation method based on super relation learning network

Abstract

The invention discloses a scene graph generation method based on super relation learning. The method comprises the following steps: 1, target interaction is enhanced through a target self-attention network, and features of targets are fused; 2, interaction between the target and the relation is enhanced through the target-relation attention network, and features between the target and the relation are fused; and 3, transmission reasoning of the super relation is integrated through the super relation attention network. and 4, model training is performed, a target loss function and a relation loss function are put into an optimizer, and gradient return and updating are performed on network parameters through a back propagation algorithm. The invention provides a deep neural network for scene graph generation, and particularly provides a super relation learning network, interaction and transmission reasoning between a target and a relation are fully utilized, the reasoning capability of the relation in scene graph generation is improved, and the performance in the field of scene graph generation is greatly improved.

Description

technical field [0001] The present invention proposes a scene graph generation method (HLN) based on Hyper-relationship Learning Network. HLN originates from a hypergraph, using a multi-layer attention network to interact between objects. We further propose the Object-Relation Attention Network (OR-GAT) to autonomously fuse features from the interactions between objects and relations. The present invention proposes a super-relational attention network for the first time to integrate super-relational transfer reasoning. where super-relations refer to a subset of the relations among the three objects. Through the interaction between objects, the interaction between objects and relations, and the transitive reasoning of super-relations, the predictive effect of relations is obviously improved. Background technique [0002] Scene Graph Generation (SGG) aims to detect objects and predict object relationships. These detected objects and relationships then constitute the scene ...

AI Technical Summary

Problems solved by technology

[0010] (3) Currently, there is no method that considers high-level connections of relations, i.e. transitive inference

However, the high-level connectivity of relations has a particularly significant impact on the performance of relation prediction

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