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.
翻译:我们建议将具有地貌特征的分解成以美元为单位的脱节分区,作为以物体为中心的演示教学方法。 为此,我们建议使用最小值-美元图形切除法作为分解法,作为线性程序。 这种方法具有地貌意识, 因为它在图像图中明确编码了邻里关系。 要解决这个图, 我们的解决方案依赖于一个高效、可缩放和可区别的二次方形编程近似值。 优化特有的切除问题使我们得以解决二次方程式, 并比一般的二次方形编程方法更高效地计算其梯度。 我们的结果显示, 我们的方法是可扩展的, 并且超越了用质谱场景和对象进行对象发现任务的现有方法 。