Test-Time Adaptation (TTA) methods improve the robustness of deep neural
networks to domain shift on a variety of tasks such as image classification or
segmentation. This work explores adapting segmentation models to a single
unlabelled image with no other data available at test-time. In particular, this
work focuses on adaptation by optimizing self-supervised losses at test-time.
Multiple baselines based on different principles are evaluated under diverse
conditions and a novel adversarial training is introduced for adaptation with
mask refinement. Our additions to the baselines result in a 3.51 and 3.28 %
increase over non-adapted baselines, without these improvements, the increase
would be 1.7 and 2.16 % only.

优化自监督损失进行测试时间段的自适应图像分割模型，采用多种基线方法及改进，并引入对抗训练来提高性能。

单幅图像测试时自适应分割

Single Image Test-Time Adaptation for Segmentation

Instance segmentation is an active topic in computer vision that is usually
solved by using supervised learning approaches over very large datasets
composed of object level masks. Obtaining such a dataset for any new domain can
be very expensive and time-consuming. In addition, models trained on certain
annotated categories do not generalize well to unseen objects. The goal of this
paper is to propose a method that can perform unsupervised discovery of
long-tail categories in instance segmentation, through learning instance
embeddings of masked regions. Leveraging rich relationship and hierarchical
structure between objects in the images, we propose self-supervised losses for
learning mask embeddings. Trained on COCO dataset without additional
annotations of the long-tail objects, our model is able to discover novel and
more fine-grained objects than the common categories in COCO. We show that the
model achieves competitive quantitative results on LVIS as compared to the
supervised and partially supervised methods.

该论文提出了一种可通过学习实例嵌入提供无监督发现实例分割中长尾类别的方法，利用图像中对象之间的丰富关系和层次结构提出了自监督损失方法训练掩码嵌入，经 COCO 数据集训练，该模型能够发现比 COCO 中的常见类别更精细和新奇的对象，并在 LVIS 评估中取得了与监督和部分监督方法相竞争的结果。

使用分层自监督的无监督实例分割长尾发现

Unsupervised Discovery of the Long-Tail in Instance Segmentation Using  Hierarchical Self-Supervision

We propose a method to learn image representations from uncurated videos. We
combine a supervised loss from off-the-shelf object detectors and
self-supervised losses which naturally arise from the video-shot-frame-object
hierarchy present in each video. We report competitive results on 19 transfer
learning tasks of the Visual Task Adaptation Benchmark (VTAB), and on 8
out-of-distribution-generalization tasks, and discuss the benefits and
shortcomings of the proposed approach. In particular, it improves over the
baseline on all 18/19 few-shot learning tasks and 8/8 out-of-distribution
generalization tasks. Finally, we perform several ablation studies and analyze
the impact of the pretrained object detector on the performance across this
suite of tasks.

该篇论文提出了一种从未加工过的视频中学习图像表示的方法，该方法将来自现成物体检测器的监督损失和自我监督损失相结合，取得了在 19 个迁移学习任务中有竞争力的结果，其中包括 18/19 的少样本学习任务和 8/8 的数据集泛化任务。