Depression is one of the most prevalent mental disorders, which seriously affects one's life.
On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information.
To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.
The challenges of this task are twofold: (1) under the interference of the missing views, it is difficult to overcome the negative impact brought by data scarcity; (2) the limited number of data exacerbates information scarcity, thereby making it harder to address the view-missing problem.
To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed.
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.
In SGCPNet, we propose the strategy of spatial-detail guided context propagation.
Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.