Exploring Disentangled Content Information for Face Forgery Detection

Convolutional neural network based face forgery detection methods have achieved remarkable results during training, but struggled to maintain comparable performance during testing. We observe that the detector is prone to focus more on content information than artifact traces, suggesting that the detector is sensitive to the intrinsic bias of the dataset, which leads to severe overfitting. Motivated by this key observation, we design an easily embeddable disentanglement framework for content information removal, and further propose a Content Consistency Constraint (C2C) and a Global Representation Contrastive Constraint (GRCC) to enhance the independence of disentangled features. Furthermore, we cleverly construct two unbalanced datasets to investigate the impact of the content bias. Extensive visualizations and experiments demonstrate that our framework can not only ignore the interference of content information, but also guide the detector to mine suspicious artifact traces and achieve competitive performance

Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective

Out-of-distribution (OOD) detection methods assume that they have test ground truths, i.e., whether individual test samples are in-distribution (IND) or OOD. However, in the real world, we do not always have such ground truths, and thus do not know which sample is correctly detected and cannot compute the metric like AUROC to evaluate the performance of different OOD detection methods. In this paper, we are the first to introduce the unsupervised evaluation problem in OOD detection, which aims to evaluate OOD detection methods in real-world changing environments without OOD labels. We propose three methods to compute Gscore as an unsupervised indicator of OOD detection performance. We further introduce a new benchmark Gbench, which has 200 real-world OOD datasets of various label spaces to train and evaluate our method. Through experiments, we find a strong quantitative correlation betwwen Gscore and the OOD detection performance. Extensive experiments demonstrate that our Gscore achieves state-of-the-art performance. Gscore also generalizes well with different IND/OOD datasets, OOD detection methods, backbones and dataset sizes. We further provide interesting analyses of the effects of backbones and IND/OOD datasets on OOD detection performance. The data and code will be available

Genetic regulation of the development of mating projections in Candida albicans.

Candida albicans is a major human fungal pathogen, capable of switching among a range of morphological types, such as the yeast form, including white and opaque cell types and the GUT (gastrointestinally induced transition) cell type, the filamentous form, including hyphal and pseudohyphal cell types, and chlamydospores. This ability is associated with its commensal and pathogenic life styles. In response to pheromone, C. albicans cells are able to form long mating projections resembling filaments. This filamentous morphology is required for efficient sexual mating. In the current study, we report the genetic regulatory mechanisms controlling the development of mating projections in C. albicans. Ectopic expression of MTLα1 in "a" cells induces the secretion of α-pheromone and promotes the development of mating projections. Using this inducible system, we reveal that members of the pheromone-sensing pathway (including the pheromone receptor), the Ste11-Hst7-Cek1/2 mediated MAPK signalling cascade, and the RAM pathway are essential for the development of mating projections. However, the cAMP/PKA signalling pathway and a number of key regulators of filamentous growth such as Hgc1, Efg1, Flo8, Tec1, Ume6, and Rfg1 are not required for mating projection formation. Therefore, despite the phenotypic similarities between filaments and mating projections in C. albicans, distinct mechanisms are involved in the regulation of these two morphologies

DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation

Due to the lack of diversity of datasets, the generalization ability of the pose estimator is poor. To solve this problem, we propose a pose augmentation solution via DH forward kinematics model, which we call DH-AUG. We observe that the previous work is all based on single-frame pose augmentation, if it is directly applied to video pose estimator, there will be several previously ignored problems: (i) angle ambiguity in bone rotation (multiple solutions); (ii) the generated skeleton video lacks movement continuity. To solve these problems, we propose a special generator based on DH forward kinematics model, which is called DH-generator. Extensive experiments demonstrate that DH-AUG can greatly increase the generalization ability of the video pose estimator. In addition, when applied to a single-frame 3D pose estimator, our method outperforms the previous best pose augmentation method. The source code has been released at https://github.com/hlz0606/DH-AUG-DH-Forward-Kinematics-Model-Driven-Augmentation-for-3D-Human-Pose-Estimation

Control Strategy of Photovoltaic DC Microgrid Based on Fuzzy EEMD

In order to improve the accuracy of power time series regulation of DC microgrid photovoltaic power generation and its hybrid energy storage system, a set of empirical mode decomposition (EEMD) control strategy based on fuzzy algorithm optimization is proposed in this paper. Through EEMD decomposition of photovoltaic power data, a set of eigenmode component functions is obtained, the EEMD power decomposition method is designed, and the EEMD optimization fuzzy control strategy is established. Finally, the effectiveness and accuracy of the controller are verified by simulation experiments. The results show that compared with the ordinary EEMD power decomposition control method, the proposed method can achieve better control effect under different power fluctuation characteristics, improve the strong randomness and fluctuation of distributed generation fluctuation, and has strong applicability