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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 5
1562-1568

Zero-shot referring image segmentation based on multimodal feature frequency domain fusion

Lin Haoran1
Liu Chunqian1
Xue Rongrong2
Xie Xunwei3
Lei Yinjie1
1. School of Electronic Information, Sichuan University, Chengdu 610065, China
2. Key Laboratory of Optical Engineering, Institute of Optics & Electronics, Chinese Academy of Sciences, Chengdu 610209, China
3. CETC Key Laboratory of Avionic Information System Technology, The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0387

Abstract

In order to solve the problem that semantic segmentation cannot handle undefined categories when applied to downstream tasks in the real world, it proposed referring image segmentation to find the corresponding target in the image according to the description of natural language text. Most of the existing methods use a cross-modal decoder to fuse the features extracted independently from the visual encoder and language encoder, but these methods cannot effectively utilize the edge features of the image and are complicated to train. CLIP is a powerful pre-trained visual language cross-modal model that can effectively extract image and text features. Therefore, this paper proposed a method of multimodal feature fusion in the frequency domain after CLIP encoding. Firstly, it used an unsupervised model to segment images, and extracted nouns in natural language text for follow-up task. Then it used the image encoder and text encoder of CLIP to encode the image and text respectively. Then it used the wavelet transform to decompose the image and text features, and decomposed and fused in the frequency domain which could make full use of the edge features of the image and the position information in the image, fused the image feature and text feature respectively in the frequency domain, then inversed the fused features. Finally, it matched the text features and image features pixel by pixel, and obtained the segmentation results, and tested on commonly used data sets. The experimental results prove that the network has achieved good results without training zero samples, and has good robustness and generalization ability.

Foundation Support

国家自然科学基金资助项目(62276176)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0387
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 5
Section: Technology of Graphic & Image
Pages: 1562-1568
Serial Number: 1001-3695(2024)05-040-1562-07

Publish History

[2023-11-02] Accepted Paper
[2024-05-05] Printed Article

Cite This Article

林浩然, 刘春黔, 薛榕融, 等. 基于多模态特征频域融合的零样本指称图像分割 [J]. 计算机应用研究, 2024, 41 (5): 1562-1568. (Lin Haoran, Liu Chunqian, Xue Rongrong, et al. Zero-shot referring image segmentation based on multimodal feature frequency domain fusion [J]. Application Research of Computers, 2024, 41 (5): 1562-1568. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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