基于条件变分推断与内省对抗学习的多样化图像描述生成

刘兵; 李穗; 刘明明; 刘浩

doi:10.12263/DZXB.20231156

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基于条件变分推断与内省对抗学习的多样化图像描述生成

学术论文 | 更新时间：2025-12-24

- 基于条件变分推断与内省对抗学习的多样化图像描述生成
- Diverse Image Captioning via Conditional Variational Inference and Introspective Adversarial Learning
- 电子学报 2024年52卷第7期页码：2219-2227
- 作者机构：
  
  1.中国矿业大学计算机科学与技术学院，江苏徐州 221116
  2.矿山数字化教育部工程研究中心，江苏徐州 221116
- 作者简介：
  
  [ "刘兵男，1981年8月出生于河南省永城市.现为中国矿业大学计算机科学与技术学院副教授.主要研究方向为机器学习和人工智能. E-mail: liubing@cumt.edu.cn" ]
  [ "李穗男，1997年6月出生于江西省赣州市.现为中国矿业大学计算机科学与技术学院硕士研究生.主要研究方向为计算机视觉和人工智能.E-mail: suili@cumt.edu.cn" ]
  [ "刘明明女，1985年4月出生于安徽省宿州市.现为中国矿业大学计算机科学与技术学院博士后.主要研究方向为深度学习和图像处理. E-mail: jsjzi_lmm@126.com" ]
  [ "刘浩男，1994年12月出生于河南省永城市.现为中国矿业大学计算机科学与技术学院博士.主要研究方向为深度学习. E-mail: TB20170007B4@cumt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62276266;61801198)
- DOI：10.12263/DZXB.20231156
  中图分类号： TP391
- 收稿：2023-12-12，
  
  修回：2024-05-19，
  
  纸质出版：2024-07-25
- 稿件说明：
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刘兵, 李穗, 刘明明, 等. 基于条件变分推断与内省对抗学习的多样化图像描述生成[J]. 电子学报, 2024, 52(07): 2219-2227.

LIU Bing, LI Sui, LIU Ming-ming, et al. Diverse Image Captioning via Conditional Variational Inference and Introspective Adversarial Learning[J]. Acta Electronica Sinica, 2024, 52(07): 2219-2227.
刘兵, 李穗, 刘明明, 等. 基于条件变分推断与内省对抗学习的多样化图像描述生成[J]. 电子学报, 2024, 52(07): 2219-2227. DOI：10.12263/DZXB.20231156

LIU Bing, LI Sui, LIU Ming-ming, et al. Diverse Image Captioning via Conditional Variational Inference and Introspective Adversarial Learning[J]. Acta Electronica Sinica, 2024, 52(07): 2219-2227. DOI：10.12263/DZXB.20231156

摘要

现有多样化图像描述生成方法受到隐空间表示能力和评价指标制约，很难同时兼顾描述生成的多样性和准确性.为此，本文提出了一种新的多样化图像描述生成模型，该模型由一个条件变分推断编码器和一个生成器组成.编码器利用全局注意力学习每个单词的隐向量空间，以提升模型对描述多样化的建模能力.生成器根据给定图像和序列隐向量生成多样化的描述语句.同时，引入内省对抗学习的思想，条件变分推断编码器同时作为鉴别器来区分真实描述和生成的描述，赋予模型自我评价生成的描述语句的能力，克服预定义评价指标的局限性.在MSCOCO数据集上的实验表明，与传统方法相比，在随机生成100个描述语句时，多样性指标mBLEU（mutual overlap-BiLingual Evaluation Understudy）提升了1.9%，同时准确性指标CIDEr（Consensus-based Image Description Evaluation）显著提升了7.5%.与典型多模态大模型相比，所提出方法在较小参数量的条件下更适用于生成多样化的陈述性描述语句.

Abstract

Limited by the latent space modeling ability and pre-defined diversity metrics

most diverse image captioning models fail to achieve a balance between diversity and accuracy. To this end

we propose a novel diverse image captioning framework

which consists of a transformer based variational inference encoder and a generator. Specifically

the variational inference network aims to learn a latent space for each word to enhance the ability of caption diversity modeling

while the generator network produces diverse captions conditioned on each image and a sequence of latent variables. To overcome the limitation of pre-defined metrics

we introduce introspective adversarial learning into the proposed model

where the variational inference network also serves as a discriminator to distinguish between the ground truth captions and those produced by the generator without extra discriminators. The proposed method is endowed the ability to self-evaluate the quality of generated captions. The experimental results on dataset MSCOCO show that compared with the conventional methods

the proposed method with 100 samples improves the mBLEU (mutual overlap-BiLingual Evaluation Understudy) scores by 1.9% and the CIDEr (Consensus-based Image Description Evaluation) scores by 7.5%

respectively. Compared with typical multimodal large models

the proposed method is more suitable for generating diverse declarative descriptive captions with smaller parameters.

关键词

Keywords

references

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