学习二阶注意力上下文以实现高效的对应关系剪枝
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
AI总结：
本文提出一种基于二阶注意力上下文（ANA）的高效对应关系剪枝方法，通过推导近似公式将计算复杂度降至线性，在保持竞争性准确率的同时运行速度比LMCNet快14倍。
摘要：
对应关系剪枝旨在从一组假设对应关系中搜索一致的对应关系（内点）。由于大量离群点的空间分布杂乱无章，尤其是当假设对应关系主要由离群点组成时，这极具挑战性。在确保有效性的同时保持高效性则更具挑战。本文提出了一种有效且高效的对应关系剪枝方法。受注意力上下文在对应问题中取得成功的启发，我们首先将注意力上下文扩展为一阶注意力上下文，然后引入注意力中的注意力（ANA）思想，为对应关系剪枝建模二阶注意力上下文。与关注特征一致上下文的一阶注意力相比，二阶注意力专注于注意力权重本身，并提供了从注意力图编码一致上下文的额外来源。为了提高效率，我们为二阶注意力的朴素实现推导了两种近似公式，将立方复杂度优化为线性复杂度，使得二阶注意力在使用时的计算开销可以忽略不计。我们进一步将这些公式实现在二阶上下文层中，然后将该层合并到ANA块中。大量实验表明，我们的方法在剪除离群点方面既有效又高效，尤其是在高离群点比例的情况下。与最先进的对应关系剪枝方法LMCNet相比，我们的方法在保持竞争性准确率的同时运行速度快14倍。
英文摘要：
Correspondence pruning aims to search consistent correspondences (inliers) from a set of putative correspondences. It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers. It's more challenging to ensure effectiveness while maintaining efficiency. In this paper, we propose an effective and efficient method for correspondence pruning. Inspired by the success of attentive context in correspondence problems, we first extend the attentive context to the first-order attentive context and then introduce the idea of attention in attention (ANA) to model second-order attentive context for correspondence pruning. Compared with first-order attention that focuses on feature-consistent context, second-order attention dedicates to attention weights itself and provides an additional source to encode consistent context from the attention map. For efficiency, we derive two approximate formulations for the naive implementation of second-order attention to optimize the cubic complexity to linear complexity, such that second-order attention can be used with negligible computational overheads. We further implement our formulations in a second-order context layer and then incorporate the layer in an ANA block. Extensive experiments demonstrate that our method is effective and efficient in pruning outliers, especially in high-outlier-ratio cases. Compared with the state-of-the-art correspondence pruning approach LMCNet, our method runs 14 times faster while maintaining a competitive accuracy.
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