Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment

by   Rui Ma, et al.

Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework, they cannot apply to the cross-task BIQA scenario, where the distortion types and evaluation criteria keep changing in practical applications. This paper proposes a scalable incremental learning framework (SILF) that could sequentially conduct BIQA across multiple evaluation tasks with limited memory capacity. More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other. Each parameter subset is temporarily settled to Remember one evaluation preference toward its corresponding task, and the previously settled parameter subsets can be adaptively reused in the following BIQA to achieve better performance based on the task relevance. To suppress the unrestrained expansion of memory capacity in sequential tasks learning, we develop a scalable memory unit by gradually and selectively pruning unimportant neurons from previously settled parameter subsets, which enable us to Forget part of previous experiences and free the limited memory capacity for adapting to the emerging new tasks. Extensive experiments on eleven IQA datasets demonstrate that our proposed method significantly outperforms the other state-of-the-art methods in cross-task BIQA.


page 1

page 2

page 5


No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

Blind or no-reference image quality assessment (NR-IQA) is a fundamental...

Task-Specific Normalization for Continual Learning of Blind Image Quality Models

The computational vision community has recently paid attention to contin...

Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective

We aim at advancing blind image quality assessment (BIQA), which predict...

A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System

The problem of stereoscopic image quality assessment, which finds applic...

Image quality assessment for machine learning tasks using meta-reinforcement learning

In this paper, we consider image quality assessment (IQA) as a measure o...

Continual Learning for Blind Image Quality Assessment

The explosive growth of image data facilitates the fast development of i...

Please sign up or login with your details

Forgot password? Click here to reset