114 research outputs found
Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification
Hierarchical classification (HC) assigns each object with multiple labels
organized into a hierarchical structure. The existing deep learning based HC
methods usually predict an instance starting from the root node until a leaf
node is reached. However, in the real world, images interfered by noise,
occlusion, blur, or low resolution may not provide sufficient information for
the classification at subordinate levels. To address this issue, we propose a
novel semantic guided level-category hybrid prediction network (SGLCHPN) that
can jointly perform the level and category prediction in an end-to-end manner.
SGLCHPN comprises two modules: a visual transformer that extracts feature
vectors from the input images, and a semantic guided cross-attention module
that uses categories word embeddings as queries to guide learning
category-specific representations. In order to evaluate the proposed method, we
construct two new datasets in which images are at a broad range of quality and
thus are labeled to different levels (depths) in the hierarchy according to
their individual quality. Experimental results demonstrate the effectiveness of
our proposed HC method.Comment: 3 figure
Mining and Predicting Smart Device User Behavior
Three types of user behavior are mined in this paper: application usage, smart device usage and periodicity of user behavior. When mining application usage, the application installation, most frequently used applications and application correlation are analyzed. The application usage is long-tailed. When mining the device usage, the mean, variance and autocorrelation are calculated both for duration and interval. Both the duration and interval are long-tailed but only duration satisfies power-law distribution. Meanwhile, the autocorrelation of both duration and interval is weak, which makes predicting user behavior based on adjacent behavior not so reasonable in related works. Then DFT (Discrete Fourier Transform) is utilized to analyze the periodicity of user behavior and results show that the most obvious periodicity is 24 hours, which is in agreement with related works. Based on the results above, an improved user behavior predicting model is proposed based on Chebyshev inequality. Experiment results show that the performance is good in accurate rate and recall rate
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