Image generation method based on improved graph convolution network

An image generation, convolutional network technology, applied in the field of image processing, can solve problems such as object overlap, object missing, artifacts, etc.

Pending Publication Date: 2021-03-09
JIANGNAN UNIV
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Problems solved by technology

But there are artifacts, overlapping objects, missing objects, etc. in the results

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  • Image generation method based on improved graph convolution network
  • Image generation method based on improved graph convolution network
  • Image generation method based on improved graph convolution network

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Embodiment Construction

[0068] In order to verify the effectiveness of this method, experiments were carried out on the Visual Genome dataset. This method uses IS (Inception score) and FID (Fréchet Inception Distance) as quantitative evaluation indicators. The IS evaluation index mainly measures the diversity of images generated by the model. , the larger the IS value, the better the diversity of the generated image; the FID evaluation index mainly measures the quality of the image generated by the model, and the smaller the FID value, the better the quality of the generated image. The word vectors of this method are all pre-trained GloVe word vectors, and the vector dimension is selected as d=300. All words not in the word vector dictionary are randomly initialized with 300-dimensional word vectors uniformly distributed between [-1,1].

[0069] Step 1: Build the MDGCN model

[0070] Step 2: Train the MDGCN model

[0071] Set the hyperparameters, input the training set to the MDGCN model, obtain the...

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Abstract

The invention discloses an image generation method based on an improved graph convolutional network. The method comprises the following steps: 1, building an input layer, and enabling words in a sentence to be mapped into a low-dimensional and continuous word vector through a pre-trained word vector; 2, establishing a Bi-LSTM layer, and mixing semantic information through the Bi-LSTM layer; 3, constructing implicit layer representation of a target vector: firstly, syntactically mixing information of a target and other words in a sentence through a GCN layer, and then calculating context representation related to the target by using an attention mechanism; 4, constructing an MDGCN layer: constructing a multi-target dependency graph of the sentence according to the dependency syntax tree, and modeling a plurality of targets of the same sentence by using a graph convolution network according to the multi-target dependency graph; 5, establishing an output layer, converting a dimension by using a full connection layer, and converting the dimension into probability representation through a softmax function; and 6, training the model, and taking a cross entropy error function and L2 weight recession as a loss function together.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an image generation method based on an improved graph convolutional network. Background technique [0002] Computer vision includes many fields such as image generation, semantic segmentation, and target detection. Among them, guiding image generation through natural language description has always been a challenging task in the field of image generation. In recent years, the emergence of deep learning has promoted natural language description-guided image generation. development and has made great progress. [0003] At this stage, Generative Adversarial Network (GAN) has been widely used in the field of image generation. Image generation guided by text description is a hot research field in recent years. Its main task is to generate a picture corresponding to the description content through a text description. The image generation method guided by text description mainly uses t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T11/20G06F40/211G06F40/289G06N3/04
CPCG06T11/206G06F40/211G06F40/289G06N3/044G06N3/045
Inventor 肖志勇张立柴志雷刘登峰吴秦陈璟
Owner JIANGNAN UNIV
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