Symmetry Encoder-Decoder Network with Attention Mechanism for Fast Video Object Segmentation

Guo, Mingyue; Zhang, Dejun; Sun, Jun; Wu, Yonghong

doi:10.3390/sym11081006

Cited by 2 publications

(2 citation statements)

References 59 publications

(106 reference statements)

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“…Capsule-HandNet could be adopted for many applications related to hand pose recognition, such as gesture interactions for remote controls, human computer interactions in virtual environments and virtual reality, etc. In future, we plan to optimize our network [8,54,55], deploy our network in more scenarios, such as human pose estimation [56] and video object processing [57,58], and make the network adapt to diverse types of 3D data [59].…”

Section: Discussionmentioning

confidence: 99%

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Zhang

et al. 2020

Symmetry

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Hand pose estimation from 3D data is a key challenge in computer vision as well as an essential step for human–computer interaction. A lot of deep learning-based hand pose estimation methods have made significant progress but give less consideration to the inner interactions of input data, especially when consuming hand point clouds. Therefore, this paper proposes an end-to-end capsule-based hand pose estimation network (Capsule-HandNet), which processes hand point clouds directly with the consideration of structural relationships among local parts, including symmetry, junction, relative location, etc. Firstly, an encoder is adopted in Capsule-HandNet to extract multi-level features into the latent capsule by dynamic routing. The latent capsule represents the structural relationship information of the hand point cloud explicitly. Then, a decoder recovers a point cloud to fit the input hand point cloud via a latent capsule. This auto-encoder procedure is designed to ensure the effectiveness of the latent capsule. Finally, the hand pose is regressed from the combined feature, which consists of the global feature and the latent capsule. The Capsule-HandNet is evaluated on public hand pose datasets under the metrics of the mean error and the fraction of frames. The mean joint errors of Capsule-HandNet on MSRA and ICVL datasets reach 8.85 mm and 7.49 mm, respectively, and Capsule-HandNet outperforms the state-of-the-art methods on most thresholds under the fraction of frames metric. The experimental results demonstrate the effectiveness of Capsule-HandNet for 3D hand pose estimation.

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Section: Discussionmentioning

confidence: 99%

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Zhang

et al. 2020

Symmetry

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“…In order to achieve accurate cloud detection, attention should be focused on the areas with clouds during the cloud detection process. In the field of medical image classification, the object detection, etc., attention mechanism is a very effective method [35][36][37][38] that can allocate more processing resources to the target. Attention mechanism originates from human beings visual cognitive science.…”

Section: Introductionmentioning

confidence: 99%

Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

et al. 2020

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Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research.

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Symmetry Encoder-Decoder Network with Attention Mechanism for Fast Video Object Segmentation

Cited by 2 publications

References 59 publications

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

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