We propose a point cloud annotation framework that employs human-in-loop learning to enable the creation of large point cloud datasets with per-point annotations. Sparse labels from a human annotator are iteratively propagated to generate a full segmentation of the network by fine-tuning a pre-tra...
LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration. Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll. NeurIPS 2020. [PDF] [Code] 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambi...
内容提示: arXiv:1804.02063v1 [cs.CL] 5 Apr 2018Few-Shot Text Classif i cation with Pre-Trained WordEmbeddings and a Human in the LoopKatherine Bailey and Sunny ChopraAcquia{katherine.bailey,sunny.chopra}@acquia.comA BSTRACTMost of the literature around text classif i cation treats it as a...
Although wearable robots are human-centered systems, not putting the human user in the control loop remains a major concern9. For instance, issues may arise if individuals wearing exoskeletons are unintentionally pulled or moved against their intentions. A lot of wearable robots mainly focus on the...
Here, we present a precise graph-based AAM model, namedLocalMapper, via human-in-the-loop machine learning. Apart from previous ML-based approaches, which learn the AAM without correct answers, we manually label the AAM of reaction data to train the model. While the manual labeling of a la...
Note that there are some papers that I don't list here most because I have tested it and the result is not so good(e,g. frankmocap,VIBE and so on) human 3d pose estimation simplify optical or inertial based motion capture human motion capture in 3d scene ...
With the continuous advancement of deep learning methods, more and more high-performance 3D human pose estimation methods based on deep learning have been proposed. However, due to the human occlusion of the picture and the large demand for training scale, there are still challenges in 3D human...
Volet found that while the overall achievement motivation remained relatively stable in the group of Singaporean students, more contextualized aspects of their motivation, such as self-efficacy and goal orientation for particular learning activities, changed over time in the new academic setting with its...
Identification of bacterial protein–protein interactions and predicting the structures of these complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here we developed RoseTTAFold2-Lite, a rapid deep learning model that leverages residue–resi...
Biology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set