A5101400873- 3D Shape Modeling and Analysis
- Human Pose and Action Recognition
- Advanced Vision and Imaging
- Gaze Tracking and Assistive Technology
- Sleep and Work-Related Fatigue
- EEG and Brain-Computer Interfaces
- Video Surveillance and Tracking Methods
- Anomaly Detection Techniques and Applications
- Multimodal Machine Learning Applications
- Computer Graphics and Visualization Techniques
- Speech and dialogue systems
- Advanced Decision-Making Techniques
- E-commerce and Technology Innovations
- Domain Adaptation and Few-Shot Learning
- Time Series Analysis and Forecasting
- Media Influence and Health
- Advanced Computational Techniques and Applications
- Mathematics and Applications
- AI in cancer detection
- Remote Sensing and LiDAR Applications
- Misinformation and Its Impacts
- Medical Imaging Techniques and Applications
- Geographic Information Systems Studies
- Media Influence and Politics
- Imbalanced Data Classification Techniques
China University of Petroleum, East China
2024
Institute of Software
2024
Nanjing Institute of Technology
2024
Minzu University of China
2021
Dalian Minzu University
2021
Jinhua Polytechnic
2021
Princeton University
2020
University of Michigan
2020
Fudan University
2020
Driver Monitoring System (DMS), usually equipped with a camera, is an emerging vehicle safety system that can monitor driver attentiveness and trigger timely alarms when signs of inattention are detected. Since single indicator (e.g., eye blink rate) insufficient unreliable to analyze attentiveness, almost all existing solutions train several independent models identify facial states, such as face landmark, head pose, yawning, state, etc. However, apart from neglecting the inherent...
Nowadays, driver drowsiness and distraction is considered as a major risk for fatal road accidents around the world. As result, monitoring identifying emerging an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning eye state analysis. However, existing work has investigated algorithms to detect these tasks separately was usually conducted under laboratory environments. To address this problem, we propose multi-task learning CNN...
Mamba is an efficient sequence model that rivals Transformers and demonstrates significant potential as a foundational architecture for various tasks. Quantization commonly used in neural networks to reduce size computational latency. However, applying quantization remains underexplored, existing methods, which have been effective CNN Transformer models, appear inadequate models (e.g., Quarot suffers 21% accuracy drop on Vim-T$^\dagger$ even under W8A8). We pioneered the exploration of this...
Understanding spatial relations (e.g., "laptop on table") in visual input is important for both humans and robots. Existing datasets are insufficient as they lack large-scale, high-quality 3D ground truth information, which critical learning relations. In this paper, we fill gap by constructing Rel3D: the first human-annotated dataset grounding 3D. Rel3D enables quantifying effectiveness of information predicting large-scale human data. Moreover, propose minimally contrastive data collection...
In today’s digital era, rumors spreading on social media threaten societal stability and individuals’ daily lives, especially multimodal rumors. Hence, there is an urgent need for effective rumor detection methods. However, existing approaches often overlook the insufficient diversity of samples in feature space hidden similarities differences among samples. To address such challenges, we propose MVACLNet, a Multimodal Virtual Augmentation Contrastive Learning Network. first design...
Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train network perform well on real images, because graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and placement camera. In this work, we propose new method optimizes data based what call "hybrid gradient". We parametrize vector, combine approximate gradient analytical obtain hybrid...
The essence of enterprise financial modeling is to use mathematical models classify and sort out all kinds information according the main line value creation on this basis complete analysis, prediction, evaluation situation. A reasonable model also an effective means reduce fraud. In paper, a fraud identification constructed based empirical data. process construction, primary feature set selected motivation theory, then, original obtained by Mann–Whitney test set, final from using Relief...
Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train network perform well on real images, because graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and placement camera. In this work, we propose new method optimizes data based what call "hybrid gradient". We parametrize vector, combine approximate gradient analytical obtain hybrid...
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3D occupancy perception is gaining increasing attention due to its capability offer detailed and precise environment representations. Previous weakly-supervised NeRF methods balance efficiency accuracy, with mIoU varying by 5-10 points sampling count along camera rays. Recently, real-time Gaussian splatting has gained widespread popularity in reconstruction, the prediction task can also be viewed as a reconstruction task. Consequently, we propose GSRender, which naturally employs Splatting...
In view of the threat network and information system, a security events analyze method based on neural genetic algorithm is proposed in this paper. The model used to classify amounts generated by various attack scenarios. And rule items are extracted from classification results. According these items, correlation rules automatically using algorithm. Experiment results show that can generate association absence manual intervention. automatic self-adaptive improves efficiency system.
<p indent=0mm>For the problem of absence detail texture and other high-frequency features in feature extraction process deep learning network employing up-sampling operation, a pyramid frequency fusion object detection is proposed with three networks, to balance high low information improve accuracy. The input image extracted from primary pyramid, different characteristics are formed respectively by frequencies enhancement pyramid. In transmission, used highlight protection ability...
In this paper, we address the shape-from-shading problem by training deep networks with synthetic images. Unlike conventional approaches that combine learning and imagery, propose an approach does not need any external shape dataset to render Our consists of two synergistic processes: evolution complex shapes from simple primitives, a network for shape-from-shading. The generates better guided training, while improves using evolved shapes. We show our achieves state-of-the-art performance on...