… The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we
propose warp refinement through stacked feature maps and depthwise convolution kernels. …

… the parameters of both the network and the kernel can be learned jointly using SM. Following
the … As done for SSM, we calculate the tractable solution of the kernel method when training …

… efficient matching operator cross-correlation and its variants. Besides the remarkable success,
it is important to note that the heuristic matching … (eg, convolution kernels) learned on the …

… (9) applies different 1×1 convolution kernels on spatial … a global 1 × 1 convolution kernel.
The methods above extracts … also present inferior retrieval efficiency compared with APNet. …

… kernel in place of the default inner product kernel and exploit the underlying local geometry
around each pixel. We outline four such kernels: a dense kernel … 2D models to match the …

… -efficiency and expressive power while provably maintaining the classification accuracy of
the original classifier. Mix-n-Match … Therefore, we propose an alternative data-efficient kernel …

… rise to a balance-variance Pareto-efficient version of optimal matching and to a method that
… In this section we develop kernel optimal matching (KOM) methods, which are given by …

… We perform density estimation via kernel mean matching [47]. In this experiment, we
employ fifteen UCI datasets. For these real datasets, we are neither aware of the underlying …

… We show, perhaps surprisingly, that our framework encompasses this approach, but deep
kernels allow for more general model classes which can use the data more efficiently. We also …

… (4) demonstrates superior clustering performance in various applications, we observe that
it cannot efficiently deal with MKC with incomplete kernels. For example, in some practical …