Regression-based blind image quality assessment (IQA) models are susceptible
to biased training samples, leading to a biased estimation of model parameters.
To mitigate this issue, we propose a regression-free framework for image
quality evaluation, which is founded upon retrieving similar instances by
incorporating semantic and distortion features. The motivation behind this
approach is rooted in the observation that the human visual system (HVS) has
analogous visual responses to semantically similar image contents degraded by
the same distortion. The proposed framework comprises two classification-based
modules: semantic-based classification (SC) module and distortion-based
classification (DC) module. Given a test image and an IQA database, the SC
module retrieves multiple pristine images based on semantic similarity. The DC
module then retrieves instances based on distortion similarity from the
distorted images that correspond to each retrieved pristine image. Finally, the
predicted quality score is derived by aggregating the subjective quality scores
of multiple retrieved instances. Experimental results on four benchmark
databases validate that the proposed model can remarkably outperform the
state-of-the-art regression-based models.

基于检索相似的图像实例来评估图像质量的无回归框架，包含语义分类模块和畸变分类模块，根据语义和畸变相似度检索多个图像实例并聚合主观质量评分，验证实验表明该模型显著优于基于回归的模型。

非回归式盲图像质量评估

Regression-free Blind Image Quality Assessment

Few-shot classification tasks aim to classify images in query sets based on
only a few labeled examples in support sets. Most studies usually assume that
each image in a task has a single and unique class association. Under these
assumptions, these algorithms may not be able to identify the proper class
assignment when there is no exact matching between support and query classes.
For example, given a few images of lions, bikes, and apples to classify a
tiger. However, in a more general setting, we could consider the higher-level
concept, the large carnivores, to match the tiger to the lion for semantic
classification. Existing studies rarely considered this situation due to the
incompatibility of label-based supervision with complex conception
relationships. In this work, we advance the few-shot learning towards this more
challenging scenario, the semantic-based few-shot learning, and propose a
method to address the paradigm by capturing the inner semantic relationships
using interactive psychometric learning. The experiment results on the
CIFAR-100 dataset show the superiority of our method for the semantic-based
few-shot learning compared to the baseline.

本文提出了一种基于内部语义关系的交互心理测量学方法，旨在解决少样本图像分类问题中标签监督性与复杂概念关系不兼容的挑战性场景，实验结果表明我们的方法在 CIFAR-100 数据集上有很好的表现以及优越性。