Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation

Talking head video generation aims to animate a human face in a still image with dynamic poses and expressions using motion information derived from a target-driving video, while maintaining the person's identity in the source image. However, dramatic and complex motions in the driving video cause ambiguous generation, because the still source image cannot provide sufficient appearance information for occluded regions or delicate expression variations, which produces severe artifacts and significantly degrades the generation quality. To tackle this problem, we propose to learn a global facial representation space, and design a novel implicit identity representation conditioned memory compensation network, coined as MCNet, for high-fidelity talking head generation.~Specifically, we devise a network module to learn a unified spatial facial meta-memory bank from all training samples, which can provide rich facial structure and appearance priors to compensate warped source facial features for the generation. Furthermore, we propose an effective query mechanism based on implicit identity representations learned from the discrete keypoints of the source image. It can greatly facilitate the retrieval of more correlated information from the memory bank for the compensation. Extensive experiments demonstrate that MCNet can learn representative and complementary facial memory, and can clearly outperform previous state-of-the-art talking head generation methods on VoxCeleb1 and CelebV datasets. Please check our \href{https://github.com/harlanhong/ICCV2023-MCNET}{Project}.Comment: Accepted by ICCV2023, update the reference and figure

Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection

Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern. However, existing methods rely heavily on supervised learning using large quantities of annotated image samples, which are more costly to collect for important people detection than for individual entity recognition (eg, object recognition). To overcome this problem, we propose learning important people detection on partially annotated images. Our approach iteratively learns to assign pseudo-labels to individuals in un-annotated images and learns to update the important people detection model based on data with both labels and pseudo-labels. To alleviate the pseudo-labelling imbalance problem, we introduce a ranking strategy for pseudo-label estimation, and also introduce two weighting strategies: one for weighting the confidence that individuals are important people to strengthen the learning on important people and the other for neglecting noisy unlabelled images (ie, images without any important people). We have collected two large-scale datasets for evaluation. The extensive experimental results clearly confirm the efficacy of our method attained by leveraging unlabelled images for improving the performance of important people detection

Research on the Separation and Reorganization of Wheat Protein and the Quality of Wheat Noodles

This study uses Polyacrylamide gel electrophoresis (SDS-PAGE) and acid polyacrylamide gel electrophoresis (A-PAGE) to identify the composition of high molecular weight glutenin subunits (HMW-GS)and the translocation of gliadin 1BL/1RS in two high quality wheat flour. The gliadin and glutenin are separated and extracted from wheat flour by separating and recombining, then re-proportionated with proportions. The cooking quality and texture of reconstituted noodles are analyzed to identify the relationship between gliadin, glutenin and noodle’s quality. The results show that when the protein content is constant, the water absorption of recombinant noodles is negatively correlated with (gluten: alcohol) (P＜0.05), while the dry matter loss rate is not highly relevant to gluten: alcohol. The hardness, firmness and tensile strength of recombinant noodles are positively correlated with gluten: alcohol (P＜0.05). The viscosity is negatively correlated with gluten: alcohol (P＜0.05). Meanwhile, it is found that when the properties of high molecular weight glutenin and gliadin are the same, only the composition of low molecular weight glutenin is different from the precipitation value, but the properties of reconstituted noodles are similar. Therefore, we conclude that high molecular weight glutenin has a greater impact on the quality of noodles

Improving myopia awareness via school-based myopia prevention health education among Chinese students

AIM: To investigate the myopia awareness level, knowledge, attitude, and skills at baseline and to implement and evaluate the efficacy of myopia prevention health education among Chinese students. METHODS: A total of 1000 middle school students from 2 middle schools were invited to participate in the study, and myopia prevention health education was conducted. The students were assessed at baseline, followed by a survey. The efficacy of health education was evaluated using the self-comparison method pre- and post-health education. RESULTS: The study included 957 and 850 pre- and post-health education participants, respectively. The baseline knowledge of all respondents on myopic symptoms (87.5%), myopia is a risk of eyes (72.9%), myopia prevention (91.3%), myopia increases with age (86.7%), performing periodic eye examinations (92.8%), and one first, one foot, and one inch (84.8%) significantly increased after health education (P<0.001 for all). However, the percentage of students who still did not think it necessary to take breaks after 30-40min of continuous near work was 27.0%. The opinion that “myopia can be cured” was still present in 38.3%. CONCLUSION: Implementing school-based myopia prevention health education improves knowledge, attitudes, and skills regarding myopia among Chinese middle school students