Existing image restoration approaches typically employ extensive networks
specifically trained for designated degradations. Despite being effective, such
methods inevitably entail considerable storage costs and computational
overheads due to the reliance on task-specific networks. In this work, we go
beyond this well-established framework and exploit the inherent commonalities
among image restoration tasks. The primary objective is to identify components
that are shareable across restoration tasks and augment the shared components
with modules specifically trained for individual tasks. Towards this goal, we
propose AdaIR, a novel framework that enables low storage cost and efficient
training without sacrificing performance. Specifically, a generic restoration
network is first constructed through self-supervised pre-training using
synthetic degradations. Subsequent to the pre-training phase, adapters are
trained to adapt the pre-trained network to specific degradations. AdaIR
requires solely the training of lightweight, task-specific modules, ensuring a
more efficient storage and training regimen. We have conducted extensive
experiments to validate the effectiveness of AdaIR and analyze the influence of
the pre-training strategy on discovering shareable components. Extensive
experimental results show that AdaIR achieves outstanding results on multi-task
restoration while utilizing significantly fewer parameters (1.9 MB) and less
training time (7 hours) for each restoration task. The source codes and trained
models will be released.

在这项工作中，我们提出了一种名为 AdaIR 的新框架，通过在自我监督预训练中使用合成降解物来构建通用的恢复网络，然后训练特定的适配器来适应特定的降解物，从而实现低存储成本和高效训练，达到多任务恢复的卓越结果。

AdaIR：利用适配器发现图像恢复任务的潜在相似性

AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks  with Adapters

Partially supervised instance segmentation aims to perform learning on
limited mask-annotated categories of data thus eliminating expensive and
exhaustive mask annotation. The learned models are expected to be generalizable
to novel categories. Existing methods either learn a transfer function from
detection to segmentation, or cluster shape priors for segmenting novel
categories. We propose to learn the underlying class-agnostic commonalities
that can be generalized from mask-annotated categories to novel categories.
Specifically, we parse two types of commonalities: 1) shape commonalities which
are learned by performing supervised learning on instance boundary prediction;
and 2) appearance commonalities which are captured by modeling pairwise
affinities among pixels of feature maps to optimize the separability between
instance and the background. Incorporating both the shape and appearance
commonalities, our model significantly outperforms the state-of-the-art methods
on both partially supervised setting and few-shot setting for instance
segmentation on COCO dataset.

本文提出了一种从局部标注的分类中学习通用类别 - 不可知共性的方法，该方法可以用于从少量标注数据中学习新类别的实例分割，并结合了通过检测到分割的转移函数及集群形态优化分割方法。最终，我们的模型在 COOC 数据集的部分监督设置和少量样本设置下显著优于现有方法。

形状与外观通用解析网络用于部分监督实例分割

Commonality-Parsing Network across Shape and Appearance for Partially  Supervised Instance Segmentation

Given labeled data represented by a binary matrix, we consider the task to
derive a Boolean matrix factorization which identifies commonalities and
specifications among the classes. While existing works focus on rank-one
factorizations which are either specific or common to the classes, we derive
class-specific alterations from common factorizations as well. Therewith, we
broaden the applicability of our new method to datasets whose
class-dependencies have a more complex structure. On the basis of synthetic and
real-world datasets, we show on the one hand that our method is able to filter
structure which corresponds to our model assumption, and on the other hand that
our model assumption is justified in real-world application. Our method is
parameter-free.

通过布尔矩阵因子分解，发现类别之间的共同点和规范性，提出了从共同分类中导出类别特异性修正的新方法，并在合成和真实数据集上展示其适用性和实用性。