2022 7th International Conference on Image, Vision and Computing (ICIVC)最新文献_第10页

Object Detection in Optical Remote Sensing Images Based on Improved Lightweight Neural Network 基于改进轻量级神经网络的光学遥感图像目标检测

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886739

Zhen Cheng, Jianshe Xiong, PengCheng Yang, Kai Yang, Yunnuo Chen

{"title":"Object Detection in Optical Remote Sensing Images Based on Improved Lightweight Neural Network","authors":"Zhen Cheng, Jianshe Xiong, PengCheng Yang, Kai Yang, Yunnuo Chen","doi":"10.1109/ICIVC55077.2022.9886739","DOIUrl":"https://doi.org/10.1109/ICIVC55077.2022.9886739","url":null,"abstract":"The optical remote sensing images collected by Unmanned Aerial Vehicle Remote Sensing (UAVRS) with real-time information, and object detection of the optical remote sensing images has significant development potential in the many fields such as transportation and agriculture. In addition to large objects such as buildings, small objects such as vehicles and ships can also be clearly observed in the collected high-resolution remote sensing images. This paper mainly focuses on the detection of vehicles and ships in remote sensing images, and proposes Scene-SSD based on the main principles of MobileNetV3 and SSD. In this paper, we improve the basic block bottleneck of MobileNetV3, introduce Generalized Focal Loss (GFL) function to replace the original loss function in SSD, improve the class imbalance problem and make the bounding box estimations are more precise, and the network model is trained by transfer learning to improve its generalization ability. It is experimentally illustrated that in object detection of remote sensing images, the Scene-SSD proposed in this paper is fast and the tested mAP can reach 77.9%, which is better than the MobileNetV3-SSDLite with the same network structure in the comparison test.","PeriodicalId":227073,"journal":{"name":"2022 7th International Conference on Image, Vision and Computing (ICIVC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134276795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Method of Image Recognition with Deep Learning Combined with Attention Mechanism 一种结合注意机制的深度学习图像识别改进方法

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9887045

Fang Xiaoyu, Wang Linlin, Liu Chang, Hong Tao

引用次数: 0

Research on Task-Driven Dual-Light Image Fusion and Enhancement Method under Low Illumination 低照度下任务驱动双光图像融合增强方法研究

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886778

Bokun Liu, Junyu Wei, Shaojing Su, Xiaozhong Tong

引用次数: 0

Mobile Robot Path Planning Based on the Focused Heuristic Algorithm 基于聚焦启发式算法的移动机器人路径规划

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886971

Jia-Ming Lyu, Tian Ma, Wu Zhang, Yukun Yang

引用次数: 0

Review of Researches on the Emotion Recognition and Affective Computing Based on HCI 基于HCI的情感识别与情感计算研究综述

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886306

Wenqian Lin, Yunjian Zhang

引用次数: 0

Infrared and Visible Image Fusion Based on Biological Vision 基于生物视觉的红外与可见光图像融合

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9887132

Qianqian Han, Runping Xi, Qian Chen

引用次数: 0

Learnable Upsampling-Based Point Cloud Semantic Segmentation 基于可学习上采样的点云语义分割

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886287

Xue Xiang, Wenpeng Zong, Guangyun Li

引用次数: 1

MITPose: Multi-Granularity Feature Interaction for Human Pose Estimation MITPose:用于人体姿态估计的多粒度特征交互

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9887304

Jiayu Zou, Jie Qin, Zhen Zhang, Xingang Wang

引用次数: 0

A Robust Approach for Smile Recognition via Deep Convolutional Neural Networks 基于深度卷积神经网络的微笑识别鲁棒方法

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886093

Yuanzhu Liu, Zuoli Liu, Yong Zhao, Junli Xu

引用次数: 1

Semi-Supervised Semantic Segmentation of Class-Imbalanced Images: A Hierarchical Self-Attention Generative Adversarial Network 类不平衡图像的半监督语义分割:一种层次自注意生成对抗网络

2022 7th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2022-07-26 DOI: 10.1109/ICIVC55077.2022.9886496

Lu Chai, Qinyuan Liu

{"title":"Semi-Supervised Semantic Segmentation of Class-Imbalanced Images: A Hierarchical Self-Attention Generative Adversarial Network","authors":"Lu Chai, Qinyuan Liu","doi":"10.1109/ICIVC55077.2022.9886496","DOIUrl":"https://doi.org/10.1109/ICIVC55077.2022.9886496","url":null,"abstract":"How to train models with unlabeled data and implement one trained model across several data sets are key problems in computer vision applications that require high-cost annotations. Recently, a generative model [1] proves its advantages in semi-supervised segmentation and out-of-domain generalization. However, this method becomes less effective when meet with class-imbalanced images whose foreground occupies small areas. To solve this problem, we introduce a hierarchical generative model with a self-attention mechanism to help with capturing features of foreground objects. Concretely, we apply a two-stage hierarchical generative model to perform image synthesis with the self-attention mechanism. Since attention maps are also semantic labels in segmentation fields, the hierarchical self-attention model can synthesize images and corresponding segmentation labels simultaneously. At test time, the segmentation is achieved by mapping input images into latent presentations with two encoders and synthesizing labels with the generative model. We evaluate our hierarchical model on three biomedical segmentation data sets. The experimental results demonstrate that our method outperforms other baselines on semi-supervised segmentation of class-imbalanced images, and meanwhile, pre-serves out-of-domain generalization ability.","PeriodicalId":227073,"journal":{"name":"2022 7th International Conference on Image, Vision and Computing (ICIVC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117044111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1