Predicate Debiasing in Vision-Language Models Integration for Scene Graph Generation Enhancement
This work addresses the issue of underrepresentation and bias in SGG for computer vision applications, offering an incremental improvement through a novel debiasing technique.
The paper tackles the problem of predicate bias in scene graph generation (SGG) caused by imbalanced distributions in pretrained vision-language models (VLMs), proposing a training-free method that integrates debiased VLMs with SGG models to enhance representation, resulting in significant performance improvements.
Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between objects. This complexity and diversity in SGG leads to underrepresentation, where parts of triplet labels are rare or even unseen during training, resulting in imprecise predictions. To tackle this, we propose integrating the pretrained Vision-language Models to enhance representation. However, due to the gap between pretraining and SGG, direct inference of pretrained VLMs on SGG leads to severe bias, which stems from the imbalanced predicates distribution in the pretraining language set. To alleviate the bias, we introduce a novel LM Estimation to approximate the unattainable predicates distribution. Finally, we ensemble the debiased VLMs with SGG models to enhance the representation, where we design a certainty-aware indicator to score each sample and dynamically adjust the ensemble weights. Our training-free method effectively addresses the predicates bias in pretrained VLMs, enhances SGG's representation, and significantly improve the performance.