Contextual Attention for Hand Detection in the Wild

Supreeth Narasimhaswamy, Zhengwei Wei, Yang Wang, Justin Zhang, Minh Hoai; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 9567-9576

Abstract


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/

Related Material


[pdf]
[bibtex]
@InProceedings{Narasimhaswamy_2019_ICCV,
author = {Narasimhaswamy, Supreeth and Wei, Zhengwei and Wang, Yang and Zhang, Justin and Hoai, Minh},
title = {Contextual Attention for Hand Detection in the Wild},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}