2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献

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Learning Dense Correspondence from Synthetic Environments 从合成环境中学习密集对应

2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2022-03-24 DOI: 10.1109/DICTA56598.2022.10034586

Mithun Lal, Anthony Paproki, N. Habili, L. Petersson, Olivier Salvado, C. Fookes

{"title":"Learning Dense Correspondence from Synthetic Environments","authors":"Mithun Lal, Anthony Paproki, N. Habili, L. Petersson, Olivier Salvado, C. Fookes","doi":"10.1109/DICTA56598.2022.10034586","DOIUrl":"https://doi.org/10.1109/DICTA56598.2022.10034586","url":null,"abstract":"Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing methods map manually labelled human pixels in real 2D images onto the 3D surface, which is prone to human error, and the sparsity of available annotated data often leads to sub-optimal results. We propose to solve the problem of data scarcity by training 2D-3D human mapping algorithms using automatically generated synthetic data for which exact and dense 2D-3D correspondence is known. Such a learning strategy using synthetic environments has a high generalisation potential towards real-world data. Using different camera parameter variations, background and lighting settings, we created precise ground truth data that constitutes a wider distribution. We evaluate the performance of models trained on synthetic using the Common Objects In Context (COCO) dataset and validation framework. Results show that training 2D-3D mapping network models on synthetic data is a viable alternative to using real data.","PeriodicalId":159377,"journal":{"name":"2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124405380","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

Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector 对星载多光谱云探测器的对抗性攻击

2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2021-12-03 DOI: 10.1109/DICTA56598.2022.10034592

Andrew Du, Yee Wei Law, M. Sasdelli, Bo Chen, Ken Clarke, M. Brown, Tat-Jun Chin

引用次数: 7

Spatial Transformer Networks for Curriculum Learning 课程学习的空间转换网络

2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2021-08-22 DOI: 10.1109/DICTA56598.2022.10034595

Fatemeh Azimi, J. Nies, Sebastián M. Palacio, Federico Raue, Jörn Hees, A. Dengel

{"title":"Spatial Transformer Networks for Curriculum Learning","authors":"Fatemeh Azimi, J. Nies, Sebastián M. Palacio, Federico Raue, Jörn Hees, A. Dengel","doi":"10.1109/DICTA56598.2022.10034595","DOIUrl":"https://doi.org/10.1109/DICTA56598.2022.10034595","url":null,"abstract":"Curriculum learning is a bio-inspired training technique that is widely adopted in machine learning for improved optimization and better training of neural networks regarding the convergence rate or obtained accuracy. The main concept in curriculum learning is to start the training with simpler tasks and gradually increase the level of difficulty. Therefore, a natural question is how to determine or generate these simpler tasks. In this work, we take inspiration from Spatial Transformer Networks (STNs) in order to form an easy-to-hard curriculum. As STNs have been proved capable of removing the clutter from the input images and obtaining higher accuracy in image classification tasks, we hypothesize that images processed by STNs can be seen as easier tasks and utilized in the interest of curriculum learning. To this end, we study multiple strategies developed for shaping the training curriculum, using the data generated by STNs. We perform various experiments on cluttered MNIST and Fashion-MNIST datasets, where on the former, we obtain an improvement of 3.8pp in classification accuracy compared to the baseline, indicating that STNs can be considered as a tool for generating the easy-to-hard training schedule required for curriculum learning.","PeriodicalId":159377,"journal":{"name":"2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132703987","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

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