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Detecting Hands and Recognizing Physical Contact in the Wild
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Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
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BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Solar Cell Defect Detection
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DCANet: Learning Connected Attentions for Convolutional Neural Networks
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Query-based Attention CNN for Text Similarity Map
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AMNet: Memorability Estimation with Attention
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Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search
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Contextual Attention for Hand Detection in the Wild
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.
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