A Multiscale Grouping Transformer With CLIP Latents for Remote Sensing Image Captioning

隐藏字幕 计算机科学 遥感 变压器 计算机视觉 人工智能 图像(数学) 计算机图形学(图像) 地质学 工程类 电气工程 电压
作者
Lingwu Meng,Jing Wang,Ran Meng,Yang Yang,Liang Xiao
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:62: 1-15 被引量:2
标识
DOI:10.1109/tgrs.2024.3385500
摘要

Recent progress has shown that integrating multiscale visual features with advanced Transformer architectures is a promising approach for remote sensing image captioning (RSIC). However, the lack of local modeling ability in self-attention may potentially lead to inaccurate contextual information. Moreover, the scarcity of trainable image-caption pairs poses challenges in effectively harnessing the semantic alignment between images and texts. To mitigate these issues, we propose a Multiscale Grouping Transformer with Contrastive Language-Image Pre-training (CLIP) latents (MG-Transformer) for RSIC. First of all, a CLIP image embedding and a set of region features are extracted within a Multi-level Feature Extraction module. To achieve a comprehensive image representation, a Semantic Correlation module is designed to integrate the image embedding and region features with an attention gate. Subsequently, the integrated image features are fed into a Transformer model. The Transformer encoder utilizes dilated convolutions with different dilation rates to obtain multiscale visual features. To enhance the local modeling ability of the self-attention mechanism in the encoder, we introduce a Global Grouping Attention mechanism. This mechanism incorporates a grouping operation into self-attention, allowing each attention head to focus on different contextual information. The Transformer decoder then adopts the Meshed Cross-Attention mechanism to establish relationships between various scales of visual features and text features. This facilitates the generation of captions for images by the decoder. Experimental results on three RSIC datasets demonstrate the superiority of the proposed MG-Transformer. The code will be publicly available at https://github.com/One-paper-luck/MG-Transformer.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
谁主沉浮完成签到 ,获得积分10
1秒前
2秒前
852应助全能发文章采纳,获得10
2秒前
ding应助谷歌采纳,获得10
3秒前
单薄的夜南完成签到,获得积分10
3秒前
李李李发布了新的文献求助10
4秒前
123654完成签到 ,获得积分10
4秒前
fankie发布了新的文献求助30
5秒前
shinn发布了新的文献求助10
5秒前
小伍同学完成签到,获得积分10
6秒前
王一完成签到 ,获得积分10
6秒前
6秒前
科研通AI2S应助哒哒猪采纳,获得10
6秒前
123654关注了科研通微信公众号
7秒前
上官若男应助fyl采纳,获得10
8秒前
8秒前
量子星尘发布了新的文献求助10
8秒前
666完成签到,获得积分10
9秒前
9秒前
jialin完成签到,获得积分10
9秒前
科研通AI5应助JosephLee采纳,获得10
9秒前
9秒前
EasonYan发布了新的文献求助10
10秒前
雾影觅光发布了新的文献求助10
10秒前
bkagyin应助Alice采纳,获得20
10秒前
Galaxy发布了新的文献求助10
11秒前
lys发布了新的文献求助10
11秒前
11秒前
JamesPei应助o30采纳,获得10
12秒前
12秒前
13秒前
哒哒猪完成签到,获得积分20
13秒前
nicol.z完成签到,获得积分10
13秒前
15秒前
英俊的铭应助棉花糖采纳,获得10
15秒前
小马甲应助shinn采纳,获得10
16秒前
Phantom1234完成签到,获得积分10
18秒前
深情安青应助热水泡jio采纳,获得10
18秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969458
求助须知:如何正确求助?哪些是违规求助? 3514286
关于积分的说明 11173363
捐赠科研通 3249652
什么是DOI,文献DOI怎么找? 1794948
邀请新用户注册赠送积分活动 875501
科研通“疑难数据库(出版商)”最低求助积分说明 804836