TY - GEN
T1 - Contextual attention for hand detection in the wild
AU - Narasimhaswamy, Supreeth
AU - Wei, Zhengwei
AU - Wang, Yang
AU - Zhang, Justin
AU - Nguyen, Minh Hoai
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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 large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: Https://www3.cs.stonybrook.edu/∼cvl/projects/hand-det-attention/
AB - 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 large-scale annotated hand datasets containing hands in unconstrained images for training and evaluation. We show that Hand-CNN outperforms existing methods on the newly collected datasets and the publicly available PASCAL VOC human layout dataset. Data and code: Https://www3.cs.stonybrook.edu/∼cvl/projects/hand-det-attention/
UR - https://www.scopus.com/pages/publications/85077507709
U2 - 10.1109/ICCV.2019.00966
DO - 10.1109/ICCV.2019.00966
M3 - Conference contribution
AN - SCOPUS:85077507709
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 9566
EP - 9575
BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Y2 - 27 October 2019 through 2 November 2019
ER -