The reduced number of parameters in DSGG has significant implications for its performance compared to models with higher parameter counts. Despite having fewer parameters (44.2M-215.6M depending on backbone network), DSGG outperforms models like HiLo (58.8M-230.3M) due to its innovative approach utilizing graph-aware queries and relaxed sub-graph matching for dense scene graph generation tasks.
Fewer parameters not only reduce computational complexity but also help prevent overfitting by promoting more efficient learning and generalization capabilities within the model architecture.