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CLAML:视觉语言模型下铁谱图像的自适应元学习
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作者 陈泳财 张强 +2 位作者 黄咏秋 甄先通 张磊 《广东石油化工学院学报》 2024年第4期93-99,共7页
视觉语言模型由于其出色的泛化性能,近两年在众多领域表现出很好的性能。但在专业领域数据上,如润滑油中的铁谱数据,视觉语言模型的泛化性能遇到挑战。如何在少量数据情况下快速使视觉语言模型适应特定领域,实现铁谱图像的自适应学习,... 视觉语言模型由于其出色的泛化性能,近两年在众多领域表现出很好的性能。但在专业领域数据上,如润滑油中的铁谱数据,视觉语言模型的泛化性能遇到挑战。如何在少量数据情况下快速使视觉语言模型适应特定领域,实现铁谱图像的自适应学习,是一个新的挑战。研究提出一种新的视觉语言模型和大语言模型结合的自适应元学习方法。该方法在视觉语言模型基础上,利用大语言模型重新生成文本描述,如对不同类别的铁谱数据,生成涵盖成因、形态、大小和颜色等方面的文本描述,利用多角度的铁谱线索,对视觉语言模型微调,使其更适合铁谱这样的专业数据,在专业领域架构起图像和文本之间的语义桥梁,提升零样本识别能力。并在少量样本情况下,引入自适应元学习方法,实现对铁谱图像的快速自适应,进一步提升性能。实验结果表明自适应元学习方法在铁谱图像磨损类型识别中的有效性。 展开更多
关键词 视觉语言模型 大语言模型 铁谱图像分类 零样本学习 自适应元学习
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An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process 被引量:2
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作者 邹志云 于德弘 +2 位作者 冯文强 于鲁平 郭宁 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2008年第1期62-66,共5页
The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real ... The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation (BP) neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor. 展开更多
关键词 intelligent system neural networks adaptive learning adaptive prediction bioreactor process
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Interactive image segmentation with a regression based ensemble learning paradigm 被引量:2
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作者 Jin ZHANG Zhao-hui TANG +2 位作者 Wei-hua GUI Qing CHEN Jin-ping LIU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第7期1002-1020,共19页
To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. Howeve... To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines (MARS) and smooth thin plate spline regression (TPSR). Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation. 展开更多
关键词 Interactive image segmentation Multivariate adaptive regression splines (MARS) Ensemble learning Thin-platespline regression (TPSR) Semi-supervised learning Support vector regression (SVR)
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