Object-QA: Towards High Reliable Object Quality Assessment

TL;DR

This paper introduces Object-QA, a novel method for assessing object image quality to enhance recognition tasks, demonstrating high reliability and improved performance across multiple datasets.

Contribution

It is the first to define and address the problem of object quality assessment, proposing a new approach that works with only object-level annotations.

Findings

Object-QA accurately assesses object image quality aligning with human judgment.

The method improves recognition performance by filtering low-quality images.

Validated on five datasets, showing consistent reliability and effectiveness.

Abstract

In object recognition applications, object images usually appear with different quality levels. Practically, it is very important to indicate object image qualities for better application performance, e.g. filtering out low-quality object image frames to maintain robust video object recognition results and speed up inference. However, no previous works are explicitly proposed for addressing the problem. In this paper, we define the problem of object quality assessment for the first time and propose an effective approach named Object-QA to assess high-reliable quality scores for object images. Concretely, Object-QA first employs a well-designed relative quality assessing module that learns the intra-class-level quality scores by referring to the difference between object images and their estimated templates. Then an absolute quality assessing module is designed to generate the final quality scores by aligning the quality score distributions in inter-class. Besides, Object-QA can be implemented with only object-level annotations, and is also easily deployed to a variety of object recognition tasks. To our best knowledge this is the first work to put forward the definition of this problem and conduct quantitative evaluations. Validations on 5 different datasets show that Object-QA can not only assess high-reliable quality scores according with human cognition, but also improve application performance.