BCTR: Bidirectional Conditioning Transformer for Scene Graph Generation

• URL: http://arxiv.org/abs/2407.18715v2
• Date: Sun, 17 Nov 2024 11:30:27 GMT
• Title: BCTR: Bidirectional Conditioning Transformer for Scene Graph Generation
• Authors: Peng Hao, Xiaobing Wang, Yingying Jiang, Hanchao Jia, Xiaoshuai Hao,
• Abstract summary: We propose a novel bidirectional conditioning factorization in a semantic-aligned space for Scene Graph Generation (SGG) We introduce an end-to-end scene graph generation model, the Bidirectional Conditioning Transformer (BCTR) BCTR consists of two key modules. First, the Bidirectional Conditioning Generator (BCG) performs multi-stage interactive feature augmentation between entities and predicates, enabling mutual enhancement between these predictions. Second, Random Feature Alignment (RFA) is present to regularize feature space by distilling multi-modal knowledge from pre-trained models. Within this regularized feature space, BCG is feasible to capture
• Score: 4.977568882858193
• License:
• Abstract: Scene Graph Generation (SGG) remains a challenging task due to its compositional property. Previous approaches improve prediction efficiency through end-to-end learning. However, these methods exhibit limited performance as they assume unidirectional conditioning between entities and predicates, which restricts effective information interaction. To address this limitation, we propose a novel bidirectional conditioning factorization in a semantic-aligned space for SGG, enabling efficient and generalizable interaction between entities and predicates. Specifically, we introduce an end-to-end scene graph generation model, the Bidirectional Conditioning Transformer (BCTR), to implement this factorization. BCTR consists of two key modules. First, the Bidirectional Conditioning Generator (BCG) performs multi-stage interactive feature augmentation between entities and predicates, enabling mutual enhancement between these predictions. Second, Random Feature Alignment (RFA) is present to regularize feature space by distilling multi-modal knowledge from pre-trained models. Within this regularized feature space, BCG is feasible to capture interaction patterns across diverse relationships during training, and the learned interaction patterns can generalize to unseen but semantically related relationships during inference. Extensive experiments on Visual Genome and Open Image V6 show that BCTR achieves state-of-the-art performance on both benchmarks.

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