… We initialize each regression networks to estimate affine transformation parameters as
½I2Â2;02Â1 before the training starts. M-estimator sample and consensus (MSAC) [61] is applied …

… the other hand formulate semantic correspondence as a geometric alignment problem and
directly regress parameters of a global transformation model (eg, affine and thin plate spline) …

… semantic alignment ones, where the regression of a single global geometric transformation
between images may be … Sohn, “PARN: Pyramidal affine regression networks for dense …

… [28] introduced PARN, a pyramidal model where dense affine transformation fields are … used
in the semantic correspondence literature. Specifically, we add a consensus network [45] for …

… and an auxiliary segmentation network. Affine Transformer. After extracting the feature maps
… \) and \(F_t\) and regress the parameters of an affine transformation from source image \(I_s\…

… Parn: Pyramidal affine regression networks for dense semantic correspondence. In
Proceedings of the European Conference on Computer Vision(ECCV), 2018. [17] Sunok Kim, …

… a pyramidal affine transformation regression network to compute the correspondence
hierarchically from high-level semantics … [23] introduce a recurrent alignment network that performs …

… ing objective for dense correspondence regression. Our objective … We first sample affine
transformations by selecting scale, … It is a 4-level pyramidal network operating at two image reso…

… We focus on the aspect that semantic correspondences between images may occur at
different feature levels depending on the … Parn: Pyramidal affine regression networks for dense …

… We propose a new benchmark setting specifically for learning semantic correspondence
without using any supervised ImageNet pretrained network or validation ground truth. For all …