Abstract: Modifications on triplet loss that rescale the back-propagated gradients of
special pairs have made significant progress on local descriptor learning.
However, current gradient modulation strategies are mainly static so that they
would suffer from changes of training phases or datasets. In this paper, we
propose a dynamic gradient modulation, named SDGMNet, to improve triplet loss
for local descriptor learning. The core of our method is formulating modulation
functions with statistical characteristics which are estimated dynamically.
Firstly, we perform deep analysis on back propagation of general triplet-based
loss and introduce included angle for distance measure. On this basis,
auto-focus modulation is employed to moderate the impact of statistically
uncommon individual pairs in stochastic gradient descent optimization;
probabilistic margin cuts off the gradients of proportional Siamese pairs that
are believed to reach the optimum; power adjustment balances the total weights
of negative pairs and positive pairs. Extensive experiments demonstrate that
our novel descriptor surpasses previous state-of-the-arts on standard
benchmarks including patch verification, matching and retrieval tasks.