We aim at advancing blind image quality assessment (BIQA), which predicts the
human perception of image quality without any reference information. We develop
a general and automated multitask learning scheme for BIQA to exploit auxiliary
knowledge from other tasks, in a way that the model parameter sharing and the
loss weighting are determined automatically. Specifically, we first describe
all candidate label combinations (from multiple tasks) using a textual
template, and compute the joint probability from the cosine similarities of the
visual-textual embeddings. Predictions of each task can be inferred from the
joint distribution, and optimized by carefully designed loss functions. Through
comprehensive experiments on learning three tasks - BIQA, scene classification,
and distortion type identification, we verify that the proposed BIQA method 1)
benefits from the scene classification and distortion type identification tasks
and outperforms the state-of-the-art on multiple IQA datasets, 2) is more
robust in the group maximum differentiation competition, and 3) realigns the
quality annotations from different IQA datasets more effectively. The source
code is available at this https URL

提出了一种基于多任务学习的盲图像质量评估方法，可以自动学习来自其他任务的辅助知识，通过精心设计的损失函数进行优化，实验证明该方法可以优于现有技术在多个智商数据集上，并且在群体最大区分竞赛中更具鲁棒性。

通过视觉 - 语言对应的盲图像质量评估：多任务学习视角

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

In Continual Learning (CL), a neural network is trained on a stream of data
whose distribution changes over time. In this context, the main problem is how
to learn new information without forgetting old knowledge (i.e., Catastrophic
Forgetting). Most existing CL approaches focus on finding solutions to preserve
acquired knowledge, so working on the past of the model. However, we argue that
as the model has to continually learn new tasks, it is also important to put
focus on the present knowledge that could improve following tasks learning. In
this paper we propose a new, simple, CL algorithm that focuses on solving the
current task in a way that might facilitate the learning of the next ones. More
specifically, our approach combines the main data stream with a secondary,
diverse and uncorrelated stream, from which the network can draw auxiliary
knowledge. This helps the model from different perspectives, since auxiliary
data may contain useful features for the current and the next tasks and
incoming task classes can be mapped onto auxiliary classes. Furthermore, the
addition of data to the current task is implicitly making the classifier more
robust as we are forcing the extraction of more discriminative features. Our
method can outperform existing state-of-the-art models on the most common CL
Image Classification benchmarks.

该论文提出了一种新的 Continual Learning 算法，结合多样化和不相关的输入流，以解决当前任务，从而有助于学习下一个任务，并在常见的图像分类基准测试中表现出超过现有最先进模型的表现。