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Image content question and answer method based on multi-modality low-rank dual-linear pooling

An image content, bilinear technology, applied in the field of deep neural network, can solve the problem of high computational complexity

Active Publication Date: 2017-12-15
HANGZHOU DIANZI UNIV
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Problems solved by technology

In addition, the feature fusion model based on the bilinear model has played a very good role in many fields, such as fine-grained image classification, natural language processing, and recommendation systems. here comes a big challenge

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  • Image content question and answer method based on multi-modality low-rank dual-linear pooling
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  • Image content question and answer method based on multi-modality low-rank dual-linear pooling

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Embodiment Construction

[0102] The detailed parameters of the present invention will be further specifically described below.

[0103] Such as figure 1 As shown, the present invention provides a deep neural network structure for image content question answering (Image Question Answer, IQA), and the specific steps are as follows:

[0104] The data preprocessing described in step (1) and image and text are carried out feature extraction, specifically as follows:

[0105] The COCO-VQA dataset is used here as training and testing data.

[0106] 1-1. For image data, the existing 152-layer deep residual network (Resnet-152) model is used to extract image features. Specifically, we uniformly scale the image data to 448×448 and input it into the deep residual network, and extract the output of its res5c layer as the image feature

[0107] 1-2. For question text data, we first segment the question and build a word dictionary for the question. And each question only takes the first 15 words, and if the ...

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Abstract

The invention discloses an image content question and answer method based on multi-modality low-rank dual-linear pooling. The image content question and answer method comprises the following steps of 1, conducting data preprocessing on an answer text of a question described with an image and a natural language; 2, conducting characteristic fusing on a multi-modality low-rank dual-linear pooling model; 3, establishing a neural network structure based on an MFB pooling model and a collaborative concern model; 4, training the models and utilizing a backward propagation algorithm to train neural network parameters. The neural network model aiming at an image question & answer is provided, and especially a method for conducting unified modeling on cross-media data in a question-answer in the field of the image question and answer and a network structure for studying the 'collaborative concern' to conduct modeling description on an image and question fine-grained characteristics are provided, and the best effect currently in the field of the image question and answer is obtained.

Description

technical field [0001] The present invention relates to a deep neural network for Image Question Answer (IQA), in particular to a method for uniformly modeling image-question cross-media data and learning " Collaborative concern" for modeling expression. Background technique [0002] "Cross-media" unified expression is a cross direction between the research fields of computer vision and natural language processing, which aims to bridge the "semantic gap" between different media (such as images and texts) and establish a unified semantic expression. Based on the theory and method of cross-media unified expression, some current hot research directions have been derived, such as natural description generation (ImageCaptioning), image-text cross-media retrieval (Image-Text Cross-media Retrieval) and automatic question answering of image content (Image Questioning) Answering, IQA) and so on. The goal of image natural description generation is to give an image a summary of its c...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27G06N3/08
CPCG06F16/583G06F40/289G06N3/084
Inventor 俞俊余宙项晨钞
Owner HANGZHOU DIANZI UNIV
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