Mixed-scale cross-modal fusion network for referring image segmentation

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiong Pan , Xuemei Xie , Jianxiu Yang
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引用次数: 0

Abstract

Referring image segmentation aims to segment the target by a given language expression. Recently, the bottom-up fusion network utilizes language features to highlight the most relevant regions during the visual encoder stage. However, it is not comprehensive that establish only the relationship between pixels and words. To alleviate this problem, we propose a mixed-scale cross-modal fusion method that widens the interaction between vision and language. Specially, at each stage, pyramid pooling is used to augment visual perception and improve the interaction between visual and linguistic features, thereby highlighting relevant regions in the visual data. Additionally, we employ a simple multi-scale feature fusion module to effectively combine multi-scale aligned features. Experiments conducted on Standard RIS benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the- art approaches. Moreover, we conducted experiments on different visual backbones respectively, and the proposed method yielded better and significantly improved performance results.
用于参考图像分割的混合尺度跨模态融合网络
参考图像分割旨在通过给定的语言表达来分割目标。最近,自下而上的融合网络在视觉编码器阶段利用语言特征来突出最相关的区域。然而,这种方法并不全面,只能建立像素与单词之间的关系。为了缓解这一问题,我们提出了一种混合尺度的跨模态融合方法,扩大了视觉与语言之间的互动。特别是,在每个阶段,我们都使用金字塔池来增强视觉感知,改善视觉和语言特征之间的互动,从而突出视觉数据中的相关区域。此外,我们还采用了一个简单的多尺度特征融合模块,以有效结合多尺度对齐特征。在标准 RIS 基准上进行的实验表明,与最先进的方法相比,所提出的方法取得了良好的性能。此外,我们还分别在不同的视觉骨干上进行了实验,结果表明所提出的方法性能更好,并有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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