  Scene Graph Generation (SGG) aims to explore the relationships between
objects in images and obtain scene summary graphs, thereby better serving
downstream tasks. However, the long-tailed problem has adversely affected the
scene graph's quality. The predictions are dominated by coarse-grained
relationships, lacking more informative fine-grained ones. The union region of
one object pair (i.e., one sample) contains rich and dedicated contextual
information, enabling the prediction of the sample-specific bias for refining
the original relationship prediction. Therefore, we propose a novel
Sample-Level Bias Prediction (SBP) method for fine-grained SGG (SBG). Firstly,
we train a classic SGG model and construct a correction bias set by calculating
the margin between the ground truth label and the predicted label with one
classic SGG model. Then, we devise a Bias-Oriented Generative Adversarial
Network (BGAN) that learns to predict the constructed correction biases, which
can be utilized to correct the original predictions from coarse-grained
relationships to fine-grained ones. The extensive experimental results on VG,
GQA, and VG-1800 datasets demonstrate that our SBG outperforms the
state-of-the-art methods in terms of Average@K across three mainstream SGG
models: Motif, VCtree, and Transformer. Compared to dataset-level correction
methods on VG, SBG shows a significant average improvement of 5.6%, 3.9%, and
3.2% on Average@K for tasks PredCls, SGCls, and SGDet, respectively. The code
will be available at https://github.com/Zhuzi24/SBG.
