Shengtao Xiao

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In this paper, we present a multi-stage regression-based approach for the 300 Videos in-the-Wild (300-VW) Challenge, which progressively initializes the shape from obvious landmarks with strong semantic meanings, e.g. eyes and mouth corners, to landmarks on face contour, eyebrows and nose bridge which have more challenging features. Compared with(More)
—We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature(More)
Robust visual tracking is a challenging task in computer vision. Due to the accumulation and propagation of estimation error, model drifting often occurs and degrades the tracking performance. To mitigate this problem, in this paper we propose a novel tracking method called Recurrently Target-attending Tracking (RTT). RTT attempts to identify and exploit(More)
In this technical demonstration, we propose a face swapping framework, which is able to interactively change the appearance of a face in the wild to a different person/creature's face in real time on a mobile device. To realize this objective, we develop a deep learning-based face detector which is able to accurately detect faces in the wild. Our face(More)
In this paper, an actor critic neural network control is developed for a robotic manipulator. Both system uncertainties and unknown deadzone are considered in the tracking control design. Stability of the closed-loop system is analyzed via the Lyapunov's direct method. The critic neural network is used to estimate the cost-to-go and the actor neural network(More)