论文标题
以对象为中心表示学习的可区分数学编程
Differentiable Mathematical Programming for Object-Centric Representation Learning
论文作者
论文摘要
我们建议将拓扑感知的功能分配到给定场景功能的$ K $分离分区中，作为以对象为中心表示学习的方法。为此，我们建议将最低$ s $ - $ t $图形切割用作分区方法，该方法表示为线性程序。该方法在拓扑上意识到，因为它明确编码了图像图中的邻域关系。为了解决图，剪切的解决方案依赖于有效，可扩展和可区分的二次编程近似。与一般的二次编程方法相比，特定于削减问题的优化使我们能够解决二次程序并更有效地计算其梯度。我们的结果表明，我们的方法是可扩展的，并且具有纹理场景和对象的对象发现任务上的现有方法。
We propose topology-aware feature partitioning into $k$ disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a partitioning method which is represented as a linear program. The method is topologically aware since it explicitly encodes neighborhood relationships in the image graph. To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation. Optimizations specific to cut problems allow us to solve the quadratic programs and compute their gradients significantly more efficiently compared with the general quadratic programming approach. Our results show that our approach is scalable and outperforms existing methods on object discovery tasks with textured scenes and objects.
