论文标题
毕竟，只有最后一个神经元很重要：比较场景图的多模式融合函数
After All, Only The Last Neuron Matters: Comparing Multi-modal Fusion Functions for Scene Graph Generation
论文作者
论文摘要
从对象分割到单词矢量表示，场景图生成（SGG）成为基于众多研究结果的复杂任务。在本文中，我们关注该模型的最后一个模块：融合函数。后者的作用是结合三个隐藏状态。我们进行消融测试以比较不同的实现。首先，我们使用总和和门函数重现了最新结果。然后，我们通过添加更多模型 - 不足的函数来扩展原始解决方案：MFB和GATE之间的改编版本和混合物。根据最先进的配置，DIST执行了最佳召回 @ K，这使其现在成为最先进的一部分。
From object segmentation to word vector representations, Scene Graph Generation (SGG) became a complex task built upon numerous research results. In this paper, we focus on the last module of this model: the fusion function. The role of this latter is to combine three hidden states. We perform an ablation test in order to compare different implementations. First, we reproduce the state-of-the-art results using SUM, and GATE functions. Then we expand the original solution by adding more model-agnostic functions: an adapted version of DIST and a mixture between MFB and GATE. On the basis of the state-of-the-art configuration, DIST performed the best Recall @ K, which makes it now part of the state-of-the-art.
