Learning with semantic ambiguity for unbiased scene graph generation.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Scene graph generation (SGG) identifies objects and their relationships in images.
• SGG faces challenges with long-tail relation distributions and ambiguous semantic relations.
• Current methods struggle to represent semantic similarities between relations using one-hot labels.

Purpose of the Study:

• To propose a model-agnostic method, Mixup and Balanced Relation Learning (MBRL), to enhance SGG performance.
• To address the limitations of long-tail distributions and semantic ambiguities in relation prediction.
• To improve the accuracy and robustness of scene graph generation models.

Main Methods:

• MBRL assigns soft labels to samples with ambiguous semantic relations.
• It optimizes model training by adjusting loss weights for fine-grained and low-frequency relations.
• The method is model-agnostic, allowing integration with various SGG architectures.

Main Results:

• MBRL demonstrates superior performance over state-of-the-art approaches on SGG tasks.
• Significant improvements were observed in relation prediction accuracy.
• The method effectively handles imbalanced data distributions in relation categories.

Conclusions:

• MBRL offers an effective solution for improving scene graph generation.
• The approach enhances the ability of models to predict diverse and semantically similar relations.
• MBRL provides a valuable tool for advancing visual understanding in AI.