Attentional 2D-Human Pose Estimation出自哪里?
时间: 2024-05-22 21:10:48 浏览: 16
Attentional 2D-Human Pose Estimation是由日本东京大学的研究人员提出的。具体来说,该方法是由Takuya Akiba、Yohei Kakiuchi、Toshihiko Yamasaki和Kiyoharu Aizawa等人在2019年发表的论文《Attentional 2D-Human Pose Estimation》中提出的。
相关问题
Attentional 2D-Human Pose Estimation
Attentional 2D-Human Pose Estimation refers to a technique used in computer vision and deep learning to estimate the pose of a human body in an image or a video. The technique involves the use of a neural network that has an attention mechanism. This mechanism enables the network to focus on specific regions of the image that are relevant to the pose estimation task.
In the case of 2D-Human Pose Estimation, the neural network takes an input image and predicts the 2D coordinates of the joints of the human body. The attention mechanism allows the network to focus on the body parts that are most important for the pose estimation task, such as the head, shoulders, elbows, wrists, hips, knees, and ankles.
The attentional 2D-Human Pose Estimation technique has shown promising results in various applications, including activity recognition, emotion recognition, and human-computer interaction. It has the potential to be used in fields such as sports analysis, healthcare, and robotics.
常见的用于视频中基于注意力机制的2D人体姿态估计算法有哪些?
常见的用于视频中基于注意力机制的2D人体姿态估计算法包括:
1. Spatial Temporal Attention Pooling Network (STAPNet)
2. Multi-Level Attention Network (MLAN)
3. Spatial Temporal Graph Convolutional Network (ST-GCN)
4. Deep Multi-Person Pose Estimation with Body-Meshes
5. Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Attention Network (ECANet)
6. Pose Flow: Efficient Online Pose Tracking
7. Graph-PCNN: Two-Stage 3D Pose Estimation with Graph Pose Refinement
8. Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Attention Network (ECANet)
9. Attentional 2D-Human Pose Estimation
以上算法均采用注意力机制,可以更好地处理视频中的复杂动作和多人姿态估计问题。