Less is more: Efficient Scene Graph Generation with reparameterization

Abstract

Scene Graph Generation (SGG) aims to identify objects and their relationships in visual scenes but faces two key challenges: high computational overhead, particularly for real-time applications, and the long-tailed distribution of predicates, which biases models toward frequent relationships. To address these challenges, we propose Reparams-SGG, a lightweight and efficient network architecture composed of multi-path residual blocks. This architecture reduces computational overhead by leveraging a reparameterization strategy that minimizes sequential and parallel processing, making it highly efficient during inference. Moreover, we introduce a dynamic focal loss that dynamically adjusts the temperature scale during training to focus learning on rare predicates, promoting progressively unbiased learning. Additionally, we propose a dynamic distribution loss, compensating for learning limitations solely from one-hot distributions under data imbalance conditions. We evaluate our method on the widely-used Visual Genome and the recent PSG dataset. Reparams-SGG achieves competitive performance with significantly fewer parameters than state-of-the-art models, demonstrating its efficiency and suitability for deployment in resource-constrained environments.