AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition

The paper describes our proposed methodology for the six basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression recognition (FER) methods aim to learn the representation of expression from the artificially generated data and generalise to real data. Because of the ambiguous of the synthetic data and the objectivity of the facial Action Unit (AU), we resort to the AU information for performance boosting, and make contributions as follows. First, to adapt the model to synthetic scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose a conceptually-new framework, termed as AU-Supervised Convolutional Vision Transformers (AU-CVT), which clearly improves the performance of FER by jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The source code of our work is publicly available online: https://github.com/msy1412/ABAW

3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework

3D landmark detection plays a pivotal role in various applications such as 3D registration, pose estimation, and virtual try-on. While considerable success has been achieved in 2D human landmark detection or pose estimation, there is a notable scarcity of reported works on landmark detection in unordered 3D point clouds. This paper introduces a novel challenge, namely 3D landmark detection on human point clouds, presenting two primary contributions. Firstly, we establish a comprehensive human point cloud dataset, named HPoint103, designed to support the 3D landmark detection community. This dataset comprises 103 human point clouds created with commercial software and actors, each manually annotated with 11 stable landmarks. Secondly, we propose a Dual Cascade Point Transformer (D-CPT) model for precise point-based landmark detection. D-CPT gradually refines the landmarks through cascade Transformer decoder layers across the entire point cloud stream, simultaneously enhancing landmark coordinates with a RefineNet over local regions. Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT. Additionally, the integration of our RefineNet into existing methods consistently improves performance

Lineage Divergence and Historical Gene Flow in the Chinese Horseshoe Bat (Rhinolophus sinicus)

PMCID: PMC3581519This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Study on Friction and Wear Performance of Bionic Coupling Surface Moving Pair of 45 Steel

Abstract From the perspective of wear-resistant biological prototypes, the three non-smooth appearances of pits, stripes and grids are simplified abstractly. The shape, structure and material are organically coupled through reasonable mosaic and distribution. It was “replicated” on the surface of the 45 steel by using mechanical and laser processing methods. The wear test was conducted by using a microscopic wear tester. According to the test results, the wear mechanism of the biomimetic coupling morphology and the roles of the three coupling elements in form, structure and material in the wear resistance were analyzed. In this experimental condition, the wear resistance of the mesh shape is best under the same conditions; The coupling element of material plays the minimum of 3.52% in the wear resistance and the maximum will not exceed 64.2%; The pitch is between 0.65mm and 1.1mm. In the meanwhile, the wear resistance of the machined sample is higher than that of the laser processed smooth sample; When the distance is more than 1.2mm, the abrasion resistance of the laser processed sample is higher than that of the mechanical processing sample. This study has important reference value for the reasonable selection of coupling elements and coupling methods to improve the wear resistance of metal surfaces.</jats:p

A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis

An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively.</jats:p

A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis

An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (SVM) classifier. A coarse-to-fine scanning method was developed to detect tomatoes, followed by a proposed False Color Removal (FCR) method to remove the false-positive detections. Non-Maximum Suppression (NMS) was used to merge the overlapped results. Compared with other methods, the proposed algorithm showed substantial improvement in tomato detection. The results of tomato detection in the test images showed that the recall, precision, and F1 score of the proposed method were 90.00%, 94.41 and 92.15%, respectively