Results 91 to 100 of about 5,777,447 (338)
Action recognition by dense trajectories [PDF]
CVPR 2011, 2011 Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach
Wang, Heng +3 more
openaire +1 more source
Pathogenic Germline PALB2 and RAD50 Variants in Patients With Relapsed Ewing Sarcoma
Pediatric Blood &Cancer, EarlyView.ABSTRACT Approximately 10% of patients with Ewing sarcoma (EwS) have pathogenic germline variants. Here, we report two cases: first, a novel germline pathogenic variant in partner and localizer of BRCA2 (PALB2) in a patient with a late EwS relapse. Its impact on homologous recombination is demonstrated, and breast cancer risk is discussed.
Molly Mack +12 more
wiley +1 more source
Weakly supervised instance action recognition
Computational Visual MediaWe study the novel problem of weakly supervised instance action recognition (WSiAR) in multi-person (crowd) scenes. We specifically aim to recognize the action of each subject in the crowd, for which we propose the use of a weakly supervised method ...
Haomin Yan +4 more
doaj +1 more source
Dilated Multi-Temporal Modeling for Action Recognition
Applied Sciences, 2023 Action recognition involves capturing temporal information from video clips where the duration varies with videos for the same action. Due to the diverse scale of temporal context, uniform size kernels utilized in convolutional neural networks (CNNs ...
Tao Zhang, Yifan Wu, Xiaoqiang Li
doaj +1 more source
Multi-Scale Receptive Fields Convolutional Network for Action Recognition [PDF]
, 2023 Zhiang Dong, Miao Xie, Xiaoqiang Li
openalex +1 more source
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks [PDF]
IEEE Transactions on Image Processing, 2019 Graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, have achieved remarkable performance for skeleton-based action recognition.
Lei Shi +3 more
semanticscholar +1 more source
Feature seeding for action recognition [PDF]
2011 International Conference on Computer Vision, 2011 Progress in action recognition has been in large part due to advances in the features that drive learning-based methods. However, the relative sparsity of training data and the risk of overfitting have made it difficult to directly search for good features. In this paper we suggest using synthetic data to search for robust features that can more easily
Matikainen, Pyry +2 more
openaire +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background We describe clinical and biologic characteristics of neuroblastoma in older children, adolescents, and young adults (OCAYA); describe survival outcomes in the post‐immunotherapy era; and identify if there is an age cut‐off that best discriminates outcomes.
Rebecca J. Deyell +14 more
wiley +1 more source
RGB-D Data-Based Action Recognition: A Review
Sensors, 2021 Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction.
Muhammad Bilal Shaikh, Douglas Chai
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3D trajectories for action recognition [PDF]
2014 IEEE International Conference on Image Processing (ICIP), 2014 Recent development in affordable depth sensors opens new possibilities in action recognition problem. Depth information improves skeleton detection, therefore many authors focused on analyzing pose for action recognition. But still skeleton detection is not robust and fail in more challenging scenarios, where sensor is placed outside of optimal working
Koperski, Michal +2 more
openaire +2 more sources