Unbiased Scene Graph

Unbiased scene graph generation (USGG) aims to create accurate graphical representations of images, depicting objects and their relationships, despite the inherent imbalance in the frequency of different relationships in training data. Current research focuses on mitigating this bias through various techniques, including ensemble methods, prototype-based learning, and causal modeling, often incorporating graph neural networks and transformer architectures to better capture contextual information and handle long-tailed distributions. Advances in USGG are crucial for improving the robustness and generalizability of visual scene understanding models, impacting applications such as image captioning, visual question answering, and privacy-preserving technologies.