Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation

Semi-supervised semantic segmentation relieves the reliance on large-scale labeled data by leveraging unlabeled data. Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data. However, unreliable pseudo-labeling can undermine the semi-supervision processes. In this paper, we propose an algorithm called Multi-Level Label Correction (MLLC), which aims to use graph neural networks to capture structural relationships in Semantic-Level Graphs (SLGs) and Class-Level Graphs (CLGs) to rectify erroneous pseudo-labels. Specifically, SLGs represent semantic affinities between pairs of pixel features, and CLGs describe classification consistencies between pairs of pixel labels. With the support of proximate pattern information from graphs, MLLC can rectify incorrectly predicted pseudo-labels and can facilitate discriminative feature representations. We design an end-to-end network to train and perform this effective label corrections mechanism. Experiments demonstrate that MLLC can significantly improve supervised baselines and outperforms state-of-the-art approaches in different scenarios on Cityscapes and PASCAL VOC 2012 datasets. Specifically, MLLC improves the supervised baseline by at least 5% and 2% with DeepLabV2 and DeepLabV3+ respectively under different partition protocols.

Regional Differential Information Entropy for Super-Resolution Image Quality Assessment

PSNR and SSIM are the most widely used metrics in super-resolution problems, because they are easy to use and can evaluate the similarities between generated images and reference images. However, single image super-resolution is an ill-posed problem, there are multiple corresponding high-resolution images for the same low-resolution image. The similarities can't totally reflect the restoration effect. The perceptual quality of generated images is also important, but PSNR and SSIM do not reflect perceptual quality well. To solve the problem, we proposed a method called regional differential information entropy to measure both of the similarities and perceptual quality. To overcome the problem that traditional image information entropy can't reflect the structure information, we proposed to measure every region's information entropy with sliding window. Considering that the human visual system is more sensitive to the brightness difference at low brightness, we take $\gamma$ quantization rather than linear quantization. To accelerate the method, we reorganized the calculation procedure of information entropy with a neural network. Through experiments on our IQA dataset and PIPAL, this paper proves that RDIE can better quantify perceptual quality of images especially GAN-based images.

A Dataset and Method for Hallux Valgus Angle Estimation Based on Deep Learing

Angular measurements is essential to make a resonable treatment for Hallux valgus (HV), a common forefoot deformity. However, it still depends on manual labeling and measurement, which is time-consuming and sometimes unreliable. Automating this process is a thing of concern. However, it lack of dataset and the keypoints based method which made a great success in pose estimation is not suitable for this field.To solve the problems, we made a dataset and developed an algorithm based on deep learning and linear regression. It shows great fitting ability to the ground truth.