Human-object interaction (HOI) detection is an advanced computer vision task for detecting the relationship between human and surrounding objects. Some methods have emerged to accomplish this task with impressive results, but possess certain limitations. We analyze in detail the advantages and ...
Human-object interaction (HOI) detection is an advanced computer vision task for detecting the relationship between human and surrounding objects. Some methods have emerged to accomplish this task with impressive results, but possess certain limitations. We analyze in detail the advantages and disadvantag...
Jie Tang, Gongjian Wen (2016) Object Recognition via Classifier Interaction with Multiple Features, International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) pp. 337–340. https://doi.org/10.1109/IHMSC.2016.205 Xue Yang, Kun Fu, Hao Sun, Xian Sun, et al (2018) Obj...
backbone feature extraction network, feature fusion module, and detection head. Operating as a single-stage object detector, it necessitates only a single forward pass to predict the class and positional data of the object directly. Within the input section of RTS-Net, ...
Facial expression-based Emotion Recognition (FER) is crucial in human–computer interaction and affective computing, particularly when addressing diverse age groups. This paper introduces the Multi-Scale Vision Transformer with Contrastive Learning (MViT-CnG), an age-adaptive FER approach designed to enha...
Chandio, A.et al.Precise single-stage detector (2022).arXiv preprintarXiv:2210.04252 Ghiasi, G., Lin, T.-Y. & Le, Q. V. Nas-FPN: Learning scalable feature pyramid architecture for object detection. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition7036–704...
Yolo based human action recognition and localization Procedia computer science, 133 (2018), pp. 831-838 View PDFView articleView in ScopusGoogle Scholar [33] M. Liao, B. Shi, X. Bai, X. Wang, W. Liu Textboxes: A fast text detector with a single deep neural network Proceedings of the...
Robust hand detection in unconstrained environments is one of the most important yet challenging problems in computer vision. It is closely associated with various hand-related tasks, for example, hand gesture recognition, hand action analysis, human-machine interaction, and sign language recognition. ...
We present an approach to detecting and recognizing gestures in a stream of multi-modal data. Our approach combines a sliding-window gesture detector with features drawn from skeleton data, color imagery, and depth data produced by a first-generation Kin
DL methods used for image classification already surpass human-level accuracy when abundant labeled data are available for training26,27. However, manually labeling millions of available images is a time-consuming and laborious task; thus, obtaining a large number of manually annotated data for image...