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14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Huang, Yi Deng, Tao Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781665450850

Retinal vessel segmentation can improve the judgment ability of intelligent disease diagnosis system. Although a large number of retinal vessel segmentation models have been proposed with the development of deep learning, it is still a challenging task. In this work, we propose a new retinal vessel segmentation network via spatial-temporal and self-attention encoding modules, called STSANet, which can significantly improve the performance and robustness of segmentation. The spatial-temporal information of fundus images are extracted by a spatial-temporal encoding module in the STSANet. In addition, the internal correlation of features is captured by the Self-Attention module. By fusing spatial-temporal and self-attention features, the final result contains both spatial-temporal information and internal feature information of fundus images. The experimental results indicate that our STSANet outperforms other state-of-the-art retinal segmentation models on the published standard datasets.

关键词： Fundus image Vessel segmentation spatial-temporal encoding Self-Attention

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Multi-level spatial-temporal and attentional information deep fusion network for retinal vessel segmentation

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PHYSICS IN MEDICINE AND BIOLOGY 2023年第19期68卷 195026-195026页

作者： Huang, Yi Deng, Tao Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China

Objective. Automatic segmentation of fundus vessels has the potential to enhance the judgment ability of intelligent disease diagnosis systems. Even though various methods have been proposed, it is still a demanding task to accurately segment the fundus vessels. The purpose of our study is to develop a robust and effective method to segment the vessels in human color retinal fundus images. Approach. We present a novel multi-level spatial-temporal and attentional information deep fusion network for the segmentation of retinal vessels, called MSAFNet, which enhances segmentation performance and robustness. Our method utilizes the multi-level spatial-temporal encoding module to obtain spatial-temporal information and the Self-Attention module to capture feature correlations in different levels of our network. Based on the encoder and decoder structure, we combine these features to get the final segmentation results. Main results. Through abundant experiments on four public datasets, our method achieves preferable performance compared with other SOTA retinal vessel segmentation methods. Our Accuracy and Area Under Curve achieve the highest scores of 96.96%, 96.57%, 96.48% and 98.78%, 98.54%, 98.27% on DRIVE, CHASE_DB1, and HRF datasets. Our Specificity achieves the highest score of 98.58% and 99.08% on DRIVE and STARE datasets. Significance. The experimental results demonstrate that our method has strong learning and representation capabilities and can accurately detect retinal blood vessels, thereby serving as a potential tool for assisting in diagnosis.

关键词： fundus image vessel segmentation spatial-temporal encoding self-attention

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Event-based Timestamp Image encoding Network for Human Action Recognition and Anticipation

Event-based Timestamp Image Encoding Network for Human Actio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huang, Chaoxing Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9780738133669

Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable for human action understanding task. It is vital to encode the spatial-temporal information of event data properly and use standard computer vision tool to learn from the data. In this work, we propose a timestamp image encoding 2D network, which takes the encoded spatial-temporal images with polarity information of the event data as input and output the action label. In addition, we propose a future timestamp image generator to generate future action information to aid the model to anticipate the human action when the action is not completed. Experiment results show that our method can achieve the same level of performance as those RGB-based benchmarks on real world action recognition, and also achieve the state of the art (SOTA) result on gesture recognition. Our future timestamp image generating model can effectively improve the prediction accuracy when the action is not completed. We also provide insight discussion on the importance of motion and appearance information in action recognition and anticipation.

关键词： event camera action recognition action anticipation spatial-temporal encoding

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A Dual-Masked Auto-Encoder for Robust Motion Capture with spatial-temporal Skeletal Token Completion 22

A Dual-Masked Auto-Encoder for Robust Motion Capture with Sp...

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30th ACM International Conference on Multimedia (MM)

作者： Jiang, Junkun Chen, Jie Guo, Yike Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Imperial Coll London Data Sci Inst London England

ISBN: (纸本)9781450392037

Multi-person motion capture can be challenging due to ambiguities caused by severe occlusion, fast body movement, and complex interactions. Existing frameworks build on 2D pose estimations and triangulate to 3D coordinates via reasoning the appearance, trajectory, and geometric consistencies among multi-camera observations. However, 2D joint detection is usually incomplete and with wrong identity assignments due to limited observation angle, which leads to noisy 3D triangulation results. To overcome this issue, we propose to explore the short-range autoregressive characteristics of skeletal motion using transformer. First, we propose an adaptive, identity-aware triangulation module to reconstruct 3D joints and identify the missing joints for each identity. To generate complete 3D skeletal motion, we then propose a Dual-Masked Auto-Encoder (D-MAE) which encodes the joint status with both skeletal-structural and temporal position encoding for trajectory completion. D-MAE's flexible masking and encoding mechanism enable arbitrary skeleton definitions to be conveniently deployed under the same framework. In order to demonstrate the proposed model's capability in dealing with severe data loss scenarios, we contribute a high-accuracy and challenging motion capture dataset of multi-person interactions with severe occlusion. Evaluations on both benchmark and our new dataset demonstrate the efficiency of our proposed model, as well as its advantage against the other state-of-the-art methods.

关键词： 3D human pose estimation motion capture masked auto-encoder transformer spatial-temporal encoding

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