LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation
This work addresses the scene graph generation task for computer vision applications, offering an incremental enhancement by integrating language priors into feature refinement.
The paper tackles the problem of scene graph generation (SGG) by addressing dataset long-tail issues and complex visual features through a language-guided representation enhancement framework, resulting in consistent performance improvements on existing SGG models without needing extra knowledge.
Scene graph generation (SGG) is a sophisticated task that suffers from both complex visual features and dataset long-tail problem. Recently, various unbiased strategies have been proposed by designing novel loss functions and data balancing strategies. Unfortunately, these unbiased methods fail to emphasize language priors in feature refinement perspective. Inspired by the fact that predicates are highly correlated with semantics hidden in subject-object pair and global context, we propose LANDMARK (LANguage-guiDed representationenhanceMent frAmewoRK) that learns predicate-relevant representations from language-vision interactive patterns, global language context and pair-predicate correlation. Specifically, we first project object labels to three distinctive semantic embeddings for different representation learning. Then, Language Attention Module (LAM) and Experience Estimation Module (EEM) process subject-object word embeddings to attention vector and predicate distribution, respectively. Language Context Module (LCM) encodes global context from each word embed-ding, which avoids isolated learning from local information. Finally, modules outputs are used to update visual representations and SGG model's prediction. All language representations are purely generated from object categories so that no extra knowledge is needed. This framework is model-agnostic and consistently improves performance on existing SGG models. Besides, representation-level unbiased strategies endow LANDMARK the advantage of compatibility with other methods. Code is available at https://github.com/rafa-cxg/PySGG-cxg.