论文标题
Nodis：神经普通差异场景的理解
NODIS: Neural Ordinary Differential Scene Understanding
论文作者
论文摘要
语义图像理解是计算机视觉中充满挑战的话题。它需要检测图像中的所有对象，也需要确定它们之间的所有关系。检测到的对象，它们的标签和发现的关系可用于构建场景图，该图形提供了对图像的抽象语义解释。在先前的工作中，通过解决将分配问题提出为混合工作者线性程序来确定关系。在这项工作中，我们将该公式解释为普通微分方程（ODE）。所提出的体系结构通过通过端到端学习求解ODE的神经变体来执行场景图。它在所有三个基准任务上都取得了最新的结果：场景图生成（SGGEN），分类（SGCL）和视觉关系检测（predcls）在视觉基因组基准上。
Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as Mixed-Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. It achieves state-of-the-art results on all three benchmark tasks: scene graph generation (SGGen), classification (SGCls) and visual relationship detection (PredCls) on Visual Genome benchmark.
