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In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.

The face is one of the most reliable and easy-to-acquire biometric features, widely used for the recognition of individuals. In controlled environments facial recognition systems are highly effective, however, in real world scenarios and under varying lighting conditions, pose changes, facial expressions, occlusions and low resolution of captured images/videos, the task of recognizing faces becomes significantly complex. In this context it has been shown that certain attributes can be retrieved with a relative probability of success, being useful to complement a non conclusive result of a biometric system. In this paper we present an overview on face describable visual attributes and in particular of the so-called soft biometrics (e.g., facial marks, gender, age, skin color, and other physical characteristics). We review core issues regarding this topic, for instance what are the soft biometrics, which of them are the most robust in video surveillance and other uncontrolled scenarios, how different approaches have been addressed in the literature for their representation and classification, which datasets can be used for evaluation, which related problems remain unresolved and which are the possible ways to approach them.

Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal sound samples. Recent discriminative models based on machine identity (ID) classification have shown excellent ASD performance by leveraging strong prior knowledge like machine ID. However, such strong priors are often unavailable in real-world applications, limiting these models. To address this, we propose utilizing the imbalanced and inconsistent attribute labels from acoustic sensors, such as machine running speed and microphone model, as weak priors to train an attribute classifier. We also introduce an imbalanced compensation strategy to handle extremely imbalanced categories and ensure model trainability. Furthermore, we propose a score fusion method to enhance anomaly detection robustness. The proposed algorithm was applied in our DCASE2023 Challenge Task 2 submission, ranking sixth internationally. By exploiting acoustic sensor data attributes as weak prior knowledge, our approach provides an effective framework for robust ASD when strong priors are absent.

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly and indirectly correlated with the sensitive attribute in a complex manner, simple disparate treatment is ineffective in reducing performance disparity. This paper focuses on achieving counterfactual fairness for facial attribute classification. Each labeled input image is used to generate two synthetic replicas: one under factual assumptions about the sensitive attribute and one under counterfactual. The proposed causal graph-based attribute translation generates realistic counterfactual images that consider the complicated causal relationship among the attributes with an encoder–decoder framework. A causal graph represents complex relationships among the attributes and is used to sample factual and counterfactual facial attributes of the given face image. The encoder–decoder architecture translates the given facial image to have sampled factual or counterfactual attributes while preserving its identity. The attribute classifier is trained for fair prediction with counterfactual regularization between factual and corresponding counterfactual translated images. Extensive experimental results on the CelebA dataset demonstrate the effectiveness and interpretability of the proposed learning method for classifying multiple face attributes.

The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).

BackgroundCurrent wildfire risk research has primarily focused on hazard assessment, lacking a comprehensive framework that integrates vulnerability and adaptive capacity. Moreover, the influence of different statistical connection methods and classification criteria of factor attributes on hazard assessment has been overlooked.AimTaking Sichuan Province, China, as the study area, a comprehensive wildfire risk assessment model was constructed based on the hazard-vulnerability-adaptive capacity framework, with special focus on the effects of differences in connection methods and classification criteria of factor attributes on the modeling performance of wildfire hazard.MethodThe impact of six connection methods integrated with logistic regression (LR) on wildfire hazard assessment was explored using wildfire samples/whole region as classification criteria. Vulnerability and adaptive capacity were analyzed using techniques for ranking preferences by similarity to ideal solutions (TOPSIS), coupled with combination weights and integrated with the optimal hazard model, resulting in an integrated risk assessment framework.Key results and conclusionsSignificant differences between hazard assessment results based on different classification criteria and connection methods were found. The Point-IV-LR model, constructed using wildfire samples as classification criteria and utilizing information value (IV) coupled LR, performed the best. The risk assessment highlighted southwestern mountains as critical high-risk areas.ImplicationsThese findings provide targeted wildfire prevention strategies tailored to different risk levels in Sichuan Province.

Background Malocclusion, characterized by dental misalignment and improper occlusal relationships, significantly impacts oral health and daily functioning, with a global prevalence of 56%. Lateral cephalogram is a crucial diagnostic tool in orthodontic treatment, providing insights into various structural characteristics. Methods This study introduces a pre-training approach using multi-center lateral cephalograms for self-supervised learning, aimed at improving model generalization across diverse clinical data domains. Additionally, a multi-attribute classification network is proposed, leveraging attribute correlations to optimize parameters and enhance classification performance. Results Comprehensive evaluation on both public and clinical datasets showcases the superiority of the proposed framework, achieving an impressive average accuracy of 90.02%. The developed Self-supervised Pre-training and Multi-Attribute (SPMA) network achieves a best match ratio (MR) score of 71.38% and a low Hamming loss (HL) of 0.0425%, demonstrating its efficacy in orthodontic diagnosis from lateral cephalograms. Conclusions This work contributes significantly to advancing automated diagnostic tools in orthodontics, addressing the critical need for accurate and efficient malocclusion diagnosis. The outcomes not only improve the efficiency and accuracy of diagnosis, but also have the potential to reduce healthcare costs associated with orthodontic treatments.

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model’s potential for practical consumer metadata extraction.

Face attribute classification (FAC) is a high-profile problem in biometric verification and face retrieval. Although recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations, significant challenges still remain. Wavelet scattering transform (WST) is a promising non-learned feature extractor. It has been shown to yield more discriminative representations and outperforms the learned representations in certain tasks. Applied to the image classification task, WST can enhance subtle image texture information and create local deformation stability. This paper designs a scattering-based hybrid block, to incorporate frequency-domain (WST) and image-domain features in a channel attention manner (Squeeze-and-Excitation, SE), termed WS-SE block. Compared with CNN, WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine transform. In addition, to further exploit the relationships among the attribute labels, we propose a learning strategy from a causal view. The cause attributes defined using the causality-related information can be utilized to infer the effect attributes with a high confidence level. Ablative analysis experiments demonstrate the effectiveness of our model, and our hybrid model obtains state-of-the-art results in two public datasets.

Urban 3D geological modeling is an essential approach for quickly understanding the underground geological structure of a city and guiding underground engineering construction. Modeling methods based on multiple-point geostatistics can provide probabilistic results regarding geological structure. The traditional multiple-point geostatistics modeling approach is characterized by low efficiency and typically relies on data from geological sections or conceptual models; therefore, it cannot be well applied to practical geological exploration projects that are based primarily on borehole data. In this paper, we propose a pattern-based multiple-point geostatistics modeling method PACSIM (pattern attribute classification simulation). This method uses borehole data as the primary data. First, geological structural information is extracted based on the borehole data to establish a training image database. Next, based on the distribution patterns of geological structures, a method for establishing attribute-based pattern databases is proposed to enhance modeling accuracy. Finally, a probability constraint strategy is introduced to address the distribution of complex strata and filter out grids with high certainty, thereby further improving the modeling accuracy. Based on the aforementioned strategies, a multiple-point geostatistics modeling workflow specifically targeting underground geological structures in urban areas was designed and subjected to practical verification. The results indicate that the proposed method required less time than the PSCSIM method, and improved the modeling efficiency by 72.87% while ensuring the accuracy of the modeling results. It can accurately identify relationships among complex strata and match the stratum distribution patterns revealed by borehole data, providing a reference for high-precision geological modeling in cases with high uncertainty.