One of the widespread solutions for non-rigid tracking has a nested-loop
structure: with Gauss-Newton to minimize a tracking objective in the outer
loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear
system in the inner loop. In this paper, we employ learnable optimizations to
improve tracking robustness and speed up solver convergence. First, we upgrade
the tracking objective by integrating an alignment data term on deep features
which are learned end-to-end through CNN. The new tracking objective can
capture the global deformation which helps Gauss-Newton to jump over local
minimum, leading to robust tracking on large non-rigid motions. Second, we
bridge the gap between the preconditioning technique and learning method by
introducing a ConditionNet which is trained to generate a preconditioner such
that PCG can converge within a small number of steps. Experimental results
indicate that the proposed learning method converges faster than the original
PCG by a large margin.

通过内嵌学习算法的方式，对非刚性追踪问题进行优化，提高动态物体跟踪稳健性和求解器的收敛速度，实现了利用 CNN 学习特征进行全局形变的捕捉和快速生成预条件矩阵，在计算复杂度和实验结果方面胜过原算法。

学习优化非刚性跟踪

Learning to Optimize Non-Rigid Tracking

In the last few years, due to the growing ubiquity of unlabeled data, much
effort has been spent by the machine learning community to develop better
understanding and improve the quality of classifiers exploiting unlabeled data.
Following the manifold regularization approach, Laplacian Support Vector
Machines (LapSVMs) have shown the state of the art performance in
semi--supervised classification. In this paper we present two strategies to
solve the primal LapSVM problem, in order to overcome some issues of the
original dual formulation. Whereas training a LapSVM in the dual requires two
steps, using the primal form allows us to collapse training to a single step.
Moreover, the computational complexity of the training algorithm is reduced
from O(n^3) to O(n^2) using preconditioned conjugate gradient, where n is the
combined number of labeled and unlabeled examples. We speed up training by
using an early stopping strategy based on the prediction on unlabeled data or,
if available, on labeled validation examples. This allows the algorithm to
quickly compute approximate solutions with roughly the same classification
accuracy as the optimal ones, considerably reducing the training time. Due to
its simplicity, training LapSVM in the primal can be the starting point for
additional enhancements of the original LapSVM formulation, such as those for
dealing with large datasets. We present an extensive experimental evaluation on
real world data showing the benefits of the proposed approach.

本文提出两种解决原始 LapSVM 问题的策略，以改善在使用无标签数据进行分类时所遇到的问题，并在真实数据实验中验证了该方法的有效性。