16 research outputs found
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.Comment: CVPR 201
Establishing a Fusion Model of Attention Mechanism and Generative Adversarial Network to Estimate Students\u27 Attitudes in English Classes
With the rapid development of science and technology, artificial intelligence has been widely used in various fields and a new model of AI-aided education has been developed in the new era. In the education industry, AI-aided education can save teachers\u27 energy, improve teaching efficiency and help to refine teaching methods. In order to estimate students\u27 attitudes towards English teachers\u27 lectures, this paper proposed an AI-aided feedback system. In the constructed system, DG-Net was used to expand the data sets of students, and combined with Attention\u27s Alphapose model to collect students\u27 listening poses. The whole model provided feedback of students\u27 listening postures in English speaking and listening classes, assisting teachers to estimate students\u27 attitudes through data analysis and realizing AI-aided education in English classes
Learning Enhanced Resolution-wise features for Human Pose Estimation
Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.)
have achieved significant performance on pose estimation by combining feature
maps of various resolutions. In this paper, we propose a Resolution-wise
Attention Module (RAM) and Gradual Pyramid Refinement (GPR), to learn enhanced
resolution-wise feature maps for precise pose estimation. Specifically, RAM
learns a group of weights to represent the different importance of feature maps
across resolutions, and the GPR gradually merges every two feature maps from
low to high resolutions to regress final human keypoint heatmaps. With the
enhanced resolution-wise features learnt by CNN, we obtain more accurate human
keypoint locations. The efficacies of our proposed methods are demonstrated on
MS-COCO dataset, achieving state-of-the-art performance with average precision
of 77.7 on COCO val2017 set and 77.0 on test-dev2017 set without using extra
human keypoint training dataset.Comment: Published on ICIP 202