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Found 3 papers, 1 papers with code
Edge Detectors Can Make Deep Convolutional Neural Networks More Robust
Inspired by the principal way that human eyes recognize objects, i. e., largely relying on the shape features, this paper first employs the edge detectors as layer kernels and designs a binary edge feature branch (BEFB for short) to learn the binary edge features, which can be easily integrated into any popular backbone.
Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning
To answer this question, this paper makes a beginning effort by proposing a shallow binary feature module (SBFM for short), which can be integrated into any popular backbone.
Learning Feature Fusion for Unsupervised Domain Adaptive Person Re-identification
Unsupervised domain adaptive (UDA) person re-identification (ReID) has gained increasing attention for its effectiveness on the target domain without manual annotations.
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