  For multimodal tasks, a good feature extraction network should extract
information as much as possible and ensure that the extracted feature embedding
and other modal feature embedding have an excellent mutual understanding. The
latter is often more critical in feature fusion than the former. Therefore,
selecting the optimal feature extraction network collocation is a very
important subproblem in multimodal tasks. Most of the existing studies ignore
this problem or adopt an ergodic approach. This problem is modeled as an
optimization problem in this paper. A novel method is proposed to convert the
optimization problem into an issue of comparative upper bounds by referring to
the general practice of extreme value conversion in mathematics. Compared with
the traditional method, it reduces the time cost.
  Meanwhile, aiming at the common problem that the feature similarity and the
feature semantic similarity are not aligned in the multimodal time-series
problem, we refer to the idea of contrast learning and propose a multimodal
time-series contrastive loss(MTSC).
  Based on the above issues, We demonstrated the feasibility of our approach in
the audio-visual video parsing task. Substantial analyses verify that our
methods promote the fusion of different modal features.
