Recently, convolutional neural network (CNN) models have achieved great success in many vision tasks. However, few attempts have been made to explore CNN for online model-free object tracking without time-consuming of...
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Lie symmetry reduction of some truly ＂variable coefficient＂ wave equations which are singled out from a class of （1 ＋ 1）-dimensional variable coefficient nonlinear wave equations with respect to one and two-dimen...
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Lie symmetry reduction of some truly ＂variable coefficient＂ wave equations which are singled out from a class of （1 ＋ 1）-dimensional variable coefficient nonlinear wave equations with respect to one and two-dimensional algebras is carried out. Some classes of exact solutions of the investigated equations are found by means of both the reductions and some modern techniques such as additional equivalent transformations and hidden symmetries and so on. Conditional symmetries are also discussed.
High Dynamic Range (HDR) image is an imaging or photographic technique that can keep more intensity information than Low Dynamic Range (LDR) image. Based on the features of HDR images, we describe a technique for the ...
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In [4], Chen and Flum showed that any FPT-approximation of the k -Clique problem is not in para- AC0 and the k -DominatingSet (k -DomSet) problem could not be computed by para- AC0 circuits. It is natural to ask wheth...
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Unsupervised word representations have demonstrated improvements in predictive generalization on various NLP tasks. Much effort has been devoted to effectively learning word embeddings, but little attention has been g...
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Unsupervised word representations have demonstrated improvements in predictive generalization on various NLP tasks. Much effort has been devoted to effectively learning word embeddings, but little attention has been given to distributed character representations, although such character-level representations could be very useful for a variety of NLP applications in intrinsically "character-based" languages (e.g. Chinese and Japanese). On the other hand, most of existing models create a singleprototype representation per word, which is problematic because many words are in fact polysemous, and a single-prototype model is incapable of capturing phenomena of homonymy and polysemy. We present a neural network architecture to jointly learn character embeddings and induce context representations from large data sets. The explicitly produced context representations are further used to learn context-specific and multipleprototype character embeddings, particularly capturing their polysemous variants. Our character embeddings were evaluated on three NLP tasks of character similarity, word segmentation and named entity recognition, and the experimental results demonstrated the proposed method outperformed other competing ones on all the three tasks.
This paper investigates how to take full advantage of the tem-poral and spatial information in videos with minimal compu-tational cost in the semi-supervised video object segmentation (VOS) task. Current state-of-the-...
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In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical val...
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With the rising interest in multi-camera cross-spectral systems, cross-spectral images have been widely used in computer vision and image processing. Therefore, an effective super-resolution (SR) method provides high-...
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Previous computational models generally exclude homology out of the training set to reduce potential predictive bias. This paper proposes a hierarchical kernel to incorporate homology for more accurate similarity defi...
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