Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition最新文献_第2页

Medical Image Segmentation Approach via Transformer Knowledge Distillation 基于变压器知识蒸馏的医学图像分割方法

Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition Pub Date : 2023-04-28 DOI: 10.1145/3596286.3596292

Tianshu Zhang, Hao Wang, K. Lam, Chi-Yin Chow

引用次数: 0

Remaining useful life prediction via K-means clustering analysis and deep convolutional neural network 基于k均值聚类分析和深度卷积神经网络的剩余使用寿命预测

Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition Pub Date : 2023-04-28 DOI: 10.1145/3596286.3596297

Yuru Zhang, Chun-Ming Su, Jiajun Wu

引用次数: 1

Improving Object Detection Robustness against Natural Perturbations through Synthetic Data Augmentation 通过合成数据增强提高目标检测对自然扰动的鲁棒性

Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition Pub Date : 2023-04-28 DOI: 10.1145/3596286.3596293

N. Premakumara, Brian Jalaian, N. Suri, H. Samani

{"title":"Improving Object Detection Robustness against Natural Perturbations through Synthetic Data Augmentation","authors":"N. Premakumara, Brian Jalaian, N. Suri, H. Samani","doi":"10.1145/3596286.3596293","DOIUrl":"https://doi.org/10.1145/3596286.3596293","url":null,"abstract":"Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the problem of assessing and enhancing the robustness of object detection models against natural perturbations, such as varying lighting conditions, blur, and brightness. We analyze four state-of-the-art deep neural network models, Detr-ResNet-101, Detr-ResNet-50, YOLOv4, and YOLOv4-tiny, using the COCO 2017 dataset and ExDark dataset. By simulating synthetic perturbations with the AugLy package, we systematically explore the optimal level of synthetic perturbation required to improve the models’ robustness through data augmentation techniques. Our comprehensive ablation study meticulously evaluates the impact of synthetic perturbations on object detection models’ performance against real-world distribution shifts, establishing a tangible connection between synthetic augmentation and real-world robustness. Our findings not only substantiate the effectiveness of synthetic perturbations in improving model robustness, but also provide valuable insights for researchers and practitioners in developing more robust and reliable object detection models tailored for real-world applications.","PeriodicalId":208318,"journal":{"name":"Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133466973","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

Self Attention in U-Net for Semantic Segmentation of Low Resolution SAR Images 低分辨率SAR图像语义分割的U-Net自关注

Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition Pub Date : 2023-04-28 DOI: 10.1145/3596286.3596291

Hrishikesh Singh Yadav, Priyanshu Panchal, Divyanshu Manawat, G. S, S. S

{"title":"Self Attention in U-Net for Semantic Segmentation of Low Resolution SAR Images","authors":"Hrishikesh Singh Yadav, Priyanshu Panchal, Divyanshu Manawat, G. S, S. S","doi":"10.1145/3596286.3596291","DOIUrl":"https://doi.org/10.1145/3596286.3596291","url":null,"abstract":"The SAR image semantic segmentation using computer vision techniques has gained much popularity in the research community due to their wide applications. Despite the advancements in Deep Learning for image analysis, these models still struggle to segment SAR images due to the existence of speckle noise and a poor feature extractor. Moreover, deep learning models are challenging to train on small datasets and the performance of the model is significantly impacted by the quality of the data. This calls for the development of an effective network that can draw out critical information from the low resolution SAR images. In this regard, the present work proposes a unique Self attention module in U-Net for the semantic segmentation of low resolution SAR images.. The Self Attention Model makes use of Laplacian kernel to highlight the sharp discontinuities in the features that define the boundaries of the objects. The proposed model, employs dilated convolution layers at the initial layers, enabling the model to more effectively capture larger contextual information. With an accuracy of 0.84 and an F1-score of 0.83, the proposed model outperforms the state-of-the-art techniques in semantic segmentation of low resolution SAR images. The results clearly demonstrate the importance of the self attention module and the consideration of dilated convolution layers in the initial layers in semantic segmentation of low resolution SAR images.","PeriodicalId":208318,"journal":{"name":"Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124950825","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

Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition 2023亚洲计算机视觉、图像处理与模式识别会议论文集

Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition Pub Date : 1900-01-01 DOI: 10.1145/3596286

引用次数: 0