Learning Similarity between Scene Graphs and Images with Transformers
Scene graph generation is conventionally evaluated by (mean) Recall@K, which measures the ratio of correctly predicted triplets that appear in the ground truth. However, such triplet-oriented metrics cannot capture the global semantic information of scene graphs, and measure the similarity between images and generated scene graphs. The usability of scene graphs is therefore limited in downstream tasks. To address this issue, a framework that can measure the similarity of scene graphs and images is urgently required. Motivated by the successful application of Contrastive Language-Image Pre-training (CLIP), we propose a novel contrastive learning framework consisting of a graph Transformer and an image Transformer to align scene graphs and their corresponding images in the shared latent space. To enable the graph Transformer to comprehend the scene graph structure and extract representative features, we introduce a graph serialization technique that transforms a scene graph into a sequence with structural encoding. Based on our framework, we introduce R-Precision measuring image retrieval accuracy as a new evaluation metric for scene graph generation and establish new benchmarks for the Visual Genome and Open Images datasets. A series of experiments are further conducted to demonstrate the effectiveness of the graph Transformer, which shows great potential as a scene graph encoder.