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This paper presents a Semantic Attribute assisted video SUMmarization framework (SASUM). Compared with traditional methods, SASUM has several innovative features. Firstly, we use a natural language processing tool to discover a set of keywords from an image and text corpora to form the semantic attributes of visual contents. Secondly, we train a deep convolution neural network to extract visual features...
This paper presents an unsupervised visual theme discovery framework as a better (more compact and effective) alternative for semantic representation of visual contents. Firstly, a tag filtering algorithm was proposed focusing on the tag’s ability of visual content description. Then a spectral clustering algorithm is applied to cluster tags into visual themes based on their visual similarity...
We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent...
In this paper, a novel algorithm based on the Active Shape Model (ASM) for locating landmarks on human faces is proposed. A challenge for detecting facial features is that faces may be under different poses, this makes the local appearance of each facial landmark vary greatly. To account for these variations, we propose an adaptive-profile scheme for ASM so that facial landmarks can be detected reliably...
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