Team members working in open-source development (OSD) environments, often are geographically distributed developing their software projects. For any software project to succeed, it is important to find the right exper...
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Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and *** human interaction using RGB images is one of the most comp...
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Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and *** human interaction using RGB images is one of the most complex machine learning tasks in recent *** models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB *** paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based *** use human interaction image sequences as input data,we first perform a few denoising ***,human-to-human analyses are employed to deliver more precise *** phase follows feature engineering techniques,including diverse feature ***,we used the graph mining method for feature optimization and AdaBoost for *** tested our proposed HVAA model on two benchmark *** testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)*** second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event *** proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.
1 Introduction Artificial neural networks(ANNs,also NNs)have recently emerged as leading candidate models for deep learning,popularly used in various areas[1–3].Behind the enormous success,ANNs are generally with com...
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1 Introduction Artificial neural networks(ANNs,also NNs)have recently emerged as leading candidate models for deep learning,popularly used in various areas[1–3].Behind the enormous success,ANNs are generally with complicated structures,there being an intricate data flow through multiple linear or nonlinear components between the input layer and the output ***,it is pressing to evaluate how much a specific component contributes to the final output,termed the Credit Assignment Problem(CAP)[4]in this paper.
The field of sequential recommendation plays a crucial role in personalized recommendation systems, aiming to model users' past interactions and predict their future interactions with items or behaviors. Tradition...
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In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation ...
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Social networks not only help expand interpersonal interactions, enable data analysis, and implement intelligent recommendations, but also can deeply examine social structures and dynamic changes between individuals, ...
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作者：
Ma, XinsongZou, XinLiu, WeiweiSchool of Computer Science
National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China
Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigati...
Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigating the decision rule based on the proposed score function. Different from previous work, this paper aims to design a decision rule with rigorous theoretical guarantee and well empirical performance. Specifically, we provide a new insight for the OOD detection task from a hypothesis testing perspective and propose a novel generalized Benjamini Hochberg (g-BH) procedure with empirical p-values to solve the testing problem. Theoretically, the g-BH procedure controls false discovery rate (FDR) at pre-specified level. Furthermore, we derive an upper bound of the expectation of false positive rate (FPR) for the g-BH procedure based on the tailed generalized Gaussian distribution family, indicating that the FPR of g-BH procedure converges to zero in probability. Finally, the extensive experimental results verify the superiority of g-BH procedure over the traditional threshold-based decision rule on several OOD detection benchmarks. Copyright 2024 by the author(s)
Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attir...
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Estimating human pose in complex multi-frame situations is a challenging task and has attracted intensive research by many researchers. Although 3D human pose estimation methods have achieved remarkable results in sce...
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ISBN:
(数字)9798350374407
ISBN:
(纸本)9798350374414
Estimating human pose in complex multi-frame situations is a challenging task and has attracted intensive research by many researchers. Although 3D human pose estimation methods have achieved remarkable results in scenes based on single images, their performance often fails once these model transformations are applied to video sequences. Common problems with these models include inability to cope with motion blur, out-of-focus videos, and occlusion of human poses. In order to solve the above problems, this paper proposes a feature extraction and representation model MHMF, which is used in the feature extraction stage of the model. The initial features extracted by the backbone network HRNet-w32 are guided by the heat map to the attention layer, which improves the network’s attention to important areas for predicting key points of human posture. At the same time, the integration of the aggregation heat map and the backbone network heat map improves the spatiotemporal consistency of the key points. In addition, to improve the accuracy of mesh pose estimation under occlusion, this paper proposes a transformer-based NewDSTformer model. By adjusting the structure of the Transformer encoder, increasing the encoder level and combining it with the dynamic progressive attention masking method. The model can adapt to different input situations, handle the positional relationship of local key points, and be able to perform accurate detection even under occlusion. It was evaluated on the 3DPW data set and improved the accuracy by $0.3 \%$, indicating that this paper effectively improved the performance of 3D human mesh reconstruction.
In this paper, we systematically study the audio-visual speech separation task in a multi-speaker scenario. Given the facial information of each speaker, the goal of this task is to separate the corresponding speech f...
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