Contextual Attention for Hand Detection in the Wild
| Autor: | Yang Wang, Justin Zhang, Supreeth Narasimhaswamy, Minh Hoai Nguyen, Zhengwei Wei |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Network architecture Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Detector Computer Science - Computer Vision and Pattern Recognition Process (computing) 020207 software engineering Pattern recognition 02 engineering and technology Pascal (programming language) Object detection 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business computer computer.programming_language |
| Zdroj: | ICCV |
| DOI: | 10.1109/iccv.2019.00966 |
| Popis: | We present Hand-CNN, a novel convolutional network architecture for detecting hand masks and predicting hand orientations in unconstrained images. Hand-CNN extends MaskRCNN with a novel attention mechanism to incorporate contextual cues in the detection process. This attention mechanism can be implemented as an efficient network module that captures non-local dependencies between features. This network module can be inserted at different stages of an object detection network, and the entire detector can be trained end-to-end. We also introduce a large-scale annotated hand dataset containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on several datasets, including our hand detection benchmark and the publicly available PASCAL VOC human layout challenge. We also conduct ablation studies on hand detection to show the effectiveness of the proposed contextual attention module. 9 pages, 9 figures |
| Databáze: | OpenAIRE |
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