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Detecting facial landmarks in the video based on a hybrid framework
To dynamically detect the facial landmarks in the video, we propose a no...
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Facial Landmark Detection with Tweaked Convolutional Neural Networks
We present a novel convolutional neural network (CNN) design for facial ...
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Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking
Although heatmap regression is considered a state-of-the-art method to l...
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DeepMark++: CenterNet-based Clothing Detection
The single-stage approach for fast clothing detection as a modification ...
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GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild
Facial landmark localization is a fundamental module for face recognitio...
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Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting
We present a new Cascaded Shape Regression (CSR) architecture, namely Dy...
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Spine Landmark Localization with combining of Heatmap Regression and Direct Coordinate Regression
Landmark Localization plays a very important role in processing medical ...
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Gaussian Vector: An Efficient Solution for Facial Landmark Detection
Significant progress has been made in facial landmark detection with the development of Convolutional Neural Networks. The widely-used algorithms can be classified into coordinate regression methods and heatmap based methods. However, the former loses spatial information, resulting in poor performance while the latter suffers from large output size or high post-processing complexity. This paper proposes a new solution, Gaussian Vector, to preserve the spatial information as well as reduce the output size and simplify the post-processing. Our method provides novel vector supervision and introduces Band Pooling Module to convert heatmap into a pair of vectors for each landmark. This is a plug-and-play component which is simple and effective. Moreover, Beyond Box Strategy is proposed to handle the landmarks out of the face bounding box. We evaluate our method on 300W, COFW, WFLW and JD-landmark. That the results significantly surpass previous works demonstrates the effectiveness of our approach.
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