Given the promising results obtained by deep-learning techniques in multimedia analysis, explainability of predictions made networks has become important practical applications. We present a method to generate semantic and quantitative explanations that are easily interpretable humans. The previous work obtain such focused on contributions each feature, taking their sum be prediction result for...

In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep matching methods have made great progresses in task of semantic features. However, these usually rely on heuristically generated patterns, which may introduce unreliable relationships to hurt performance. this paper, we propose a joint learning and network, named GLAM, explore relia...

Image semantic completion is to employ remaining image information restore the damaged or missing areas. Face task usually more challenging than other inpainting problems as it requires stronger consistency. We proposed a contextual feature constrained DCGAN with paired discriminator inpaint face images, which capable of overcoming DCGAN's shortages insufficient learning capability and unstable...