Scene Graph Generation (SGG) as a critical task in image understanding, facing the challenge of head-biased prediction caused by the long-tail distribution of predicates. However, current unbiased SGG methods can easily prioritize improving the prediction of tail predicates while ignoring the substantial sacrifice in the prediction of head predicates, leading to a shift from head bias to tail bias. To address this issue, we propose a model-agnostic Head-Tail Collaborative Learning (HTCL) network that includes head-prefer and tail-prefer feature representation branches that collaborate to achieve accurate recognition of both head and tail predicates. We also propose a self-supervised learning approach to enhance the prediction ability of the tail-prefer feature representation branch by constraining tail-prefer predicate features. Specifically, self-supervised learning converges head predicate features to their class centers while dispersing tail predicate features as much as possible through contrast learning and head center loss. We demonstrate the effectiveness of our HTCL by applying it to various SGG models on VG150, Open Images V6 and GQA200 datasets. The results show that our method achieves higher mean Recall with a minimal sacrifice in Recall and achieves a new state-of-the-art overall performance. Our code is available at https://github.com/wanglei0618/HTCL.

翻译：场景图生成（SGG）作为图像理解中的关键任务，面临谓词长尾分布导致的头部偏置预测挑战。然而，当前无偏SGG方法容易优先提升尾部谓词的预测性能，却显著牺牲头部谓词的预测能力，导致从头部偏置转向尾部偏置。为解决该问题，我们提出一种模型无关的头-尾协同学习（HTCL）网络，包含趋向头部和趋向尾部的特征表示分支，通过协同合作实现对头部和尾部谓词的精准识别。我们还提出一种自监督学习方法，通过约束趋向尾部谓词特征来增强该分支的预测能力。具体而言，自监督学习通过对比学习与头部中心损失，将头部谓词特征收敛至其类别中心，同时最大程度分散尾部谓词特征。通过将HTCL应用于VG150、Open Images V6和GQA200数据集上的多种SGG模型，我们验证了其有效性。结果表明，本方法以最小化召回率牺牲实现更高平均召回率，并达到整体性能的新最优水平。我们的代码开源于https://github.com/wanglei0618/HTCL。