Triple-level Model Inferred Collaborative Network Architecture for Video Deraining

by   Pan Mu, et al.

Video deraining is an important issue for outdoor vision systems and has been investigated extensively. However, designing optimal architectures by the aggregating model formation and data distribution is a challenging task for video deraining. In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances. In particular, to mitigate the problem that existing methods cannot cover various rain streaks distribution, we first design a hyper-parameter optimization model about task variable and hyper-parameter. Based on the proposed optimization model, we design a collaborative structure for video deraining. This structure includes Dominant Network Architecture (DNA) and Companionate Network Architecture (CNA) that is cooperated by introducing an Attention-based Averaging Scheme (AAS). To better explore inter-frame information from videos, we introduce a macroscopic structure searching scheme that searches from Optical Flow Module (OFM) and Temporal Grouping Module (TGM) to help restore latent frame. In addition, we apply the differentiable neural architecture searching from a compact candidate set of task-specific operations to discover desirable rain streaks removal architectures automatically. Extensive experiments on various datasets demonstrate that our model shows significant improvements in fidelity and temporal consistency over the state-of-the-art works. Source code is available at


page 1

page 5

page 7

page 8

page 9

page 10

page 12


NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification

This paper introduces a fast and efficient network architecture, NeXtVLA...

HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search

Recent neural architecture search (NAS) based approaches have made great...

Analyzing and Improving the Pyramidal Predictive Network for Future Video Frame Prediction

The pyramidal predictive network (PPNV1) proposes an interesting tempora...

The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees

Designing code to be simplistic yet to offer choice is a tightrope walk....

Learnable Pooling Methods for Video Classification

We introduce modifications to state-of-the-art approaches to aggregating...

Auto-tuning of Deep Neural Networks by Conflicting Layer Removal

Designing neural network architectures is a challenging task and knowing...

Large-capacity and Flexible Video Steganography via Invertible Neural Network

Video steganography is the art of unobtrusively concealing secret data i...

Please sign up or login with your details

Forgot password? Click here to reset