Perceptual quality assessment of user generated content (UGC) videos is
challenging due to the requirement of large scale human annotated videos for
training. In this work, we address this challenge by first designing a
self-supervised Spatio-Temporal Visual Quality Representation Learning
(ST-VQRL) framework to generate robust quality aware features for videos. Then,
we propose a dual-model based Semi Supervised Learning (SSL) method
specifically designed for the Video Quality Assessment (SSL-VQA) task, through
a novel knowledge transfer of quality predictions between the two models. Our
SSL-VQA method uses the ST-VQRL backbone to produce robust performances across
various VQA datasets including cross-database settings, despite being learned
with limited human annotated videos. Our model improves the state-of-the-art
performance when trained only with limited data by around 10%, and by around
15% when unlabelled data is also used in SSL. Source codes and checkpoints are
available at this https URL

本研究通过自我监督的时空视觉质量表示学习 (ST-VQRL) 框架生成稳健的质量感知特征来解决用户生成内容 (UGC) 视频的感知质量评估问题，并通过双模型的半监督学习方法 (SSL-VQA) 在有限的人工注释视频的情况下，通过两个模型之间的质量预测知识传递，提高了在各种视频质量评估数据集上的性能表现。

知识引导的半监督学习用于用户生成视频的质量评估

Knowledge Guided Semi-Supervised Learning for Quality Assessment of User  Generated Videos

Surgical instrument segmentation is recognised as a key enabler to provide
advanced surgical assistance and improve computer assisted interventions. In
this work, we propose SegMatch, a semi supervised learning method to reduce the
need for expensive annotation for laparoscopic and robotic surgical images.
SegMatch builds on FixMatch, a widespread semi supervised classification
pipeline combining consistency regularization and pseudo labelling, and adapts
it for the purpose of segmentation. In our proposed SegMatch, the unlabelled
images are weakly augmented and fed into the segmentation model to generate a
pseudo-label to enforce the unsupervised loss against the output of the model
for the adversarial augmented image on the pixels with a high confidence score.
Our adaptation for segmentation tasks includes carefully considering the
equivariance and invariance properties of the augmentation functions we rely
on. To increase the relevance of our augmentations, we depart from using only
handcrafted augmentations and introduce a trainable adversarial augmentation
strategy. Our algorithm was evaluated on the MICCAI Instrument Segmentation
Challenge datasets Robust-MIS 2019 and EndoVis 2017. Our results demonstrate
that adding unlabelled data for training purposes allows us to surpass the
performance of fully supervised approaches which are limited by the
availability of training data in these challenges. SegMatch also outperforms a
range of state-of-the-art semi-supervised learning semantic segmentation models
in different labelled to unlabelled data ratios.

通过引入 SegMatch 方法，在手术仪器分割中使用半监督学习，减少了对昂贵标注的需求，并在 MICCAI 仪器分割挑战数据集上展示出性能的提升，超过了全监督学习方法和其他半监督语义分割模型。