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In this paper, we present a Self-Supervised Neural Aggregation Network (SS-NAN) for human parsing. SS-NAN adaptively learns to aggregate the multi-scale features at each pixel "address". In order to further improve the feature discriminative capacity, a self-supervised joint loss is adopted as an auxiliary learning strategy, which imposes human joint structures into parsing results without...
This paper presents the proposed solution to the "affect in the wild" challenge, which aims to estimate the affective level, i.e. the valence and arousal values, of every frame in a video. A carefully designed deep convolutional neural network (a variation of residual network) for affective level estimation of facial expressions is first implemented as a baseline. Next we use multiple memory...
Breath training is an important part in the pronunciation recovery process, we simulate the fluent of air in the the upper respiratory tract, use computational fluid dynamic to build the impaired hearing children's pronunciation recovery system, and we have tasted the reliability of the system by experimental. We study the two parts in the breath process by the dynamic fluid computational method,...
Launched from the establishment of 3D tongue model in this thesis, the tongue rehabilitation training method of hearing-impaired child combined by 3D modeling technology and sensor technology is applied and the method can reveal the tongue motor process of average person when pronouncing, guide the hearing-impaired child to do tongue pronunciation training, overcome the problem of non-visibility of...
Autism is a disorder of neural development characterized by impaired social interaction and verbal and non-verbal communication, and by restricted, repetitive or stereotyped behavior. Visual supports can alleviate these challenges by juxtaposing communication with visual cues. In this paper, a "Rehabilitation Training System (RTS)" for children with autism is developed. The "RTS"...
Constrained principal component (CPC) analysis of stochastic process extracts the most representative components from a given constraint subspace. It is an effective means to incorporate external information into principal component analysis (PCA) and is appealing in a variety of application areas. This paper proposes a novel autoassociative network to find optimal CPC solutions and compares the proposed...
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