Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World

• URL: http://arxiv.org/abs/2303.13233v2
• Date: Sat, 19 Aug 2023 14:41:36 GMT
• Title: Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World
• Authors: Qifan Yu, Juncheng Li, Yu Wu, Siliang Tang, Wei Ji, Yueting Zhuang
• Abstract summary: Scene Graph Generation (SGG) aims to extract subject, predicate, object> relationships in images for vision understanding. Existing re-balancing strategies try to handle it via prior rules but are still confined to pre-defined conditions. We propose a Cross-modal prediCate boosting (CaCao) framework, where a visually-prompted language model is learned to generate diverse fine-grained predicates.
• Score: 67.03968403301143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene Graph Generation (SGG) aims to extract <subject, predicate, object> relationships in images for vision understanding. Although recent works have made steady progress on SGG, they still suffer long-tail distribution issues that tail-predicates are more costly to train and hard to distinguish due to a small amount of annotated data compared to frequent predicates. Existing re-balancing strategies try to handle it via prior rules but are still confined to pre-defined conditions, which are not scalable for various models and datasets. In this paper, we propose a Cross-modal prediCate boosting (CaCao) framework, where a visually-prompted language model is learned to generate diverse fine-grained predicates in a low-resource way. The proposed CaCao can be applied in a plug-and-play fashion and automatically strengthen existing SGG to tackle the long-tailed problem. Based on that, we further introduce a novel Entangled cross-modal prompt approach for open-world predicate scene graph generation (Epic), where models can generalize to unseen predicates in a zero-shot manner. Comprehensive experiments on three benchmark datasets show that CaCao consistently boosts the performance of multiple scene graph generation models in a model-agnostic way. Moreover, our Epic achieves competitive performance on open-world predicate prediction. The data and code for this paper are publicly available.

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