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Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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發(fā)表于 2025-3-21 19:19:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Information Processing
副標(biāo)題29th International C
編輯Mohammad Tanveer,Sonali Agarwal,Adam Jatowt
視頻videohttp://file.papertrans.cn/664/663615/663615.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (
描述The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.?.The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications..The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements..
出版日期Conference proceedings 2023
關(guān)鍵詞pattern recognition; signal processing; computer vision; image reconstruction; neural networks; computer
版次1
doihttps://doi.org/10.1007/978-981-99-1645-0
isbn_softcover978-981-99-1644-3
isbn_ebook978-981-99-1645-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Point Cloud Completion with?Difference-Aware Point Voting guidance of the geometric commonalities shared by the observed and missing parts. The decoder finally generates the new points that can be taken as the center points for generating missing regions. Quantitative and visual results on PCN and ShapeNet-55 datasets show that our model outperforms the s
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Local-Global Interaction and?Progressive Aggregation for?Video Salient Object Detectionaliency branch for elaborate interaction. In addition, PA evolves and aggregates RGB features, OF features and up-sampled features from the higher level, and can refine saliency-related features progressively. The sophisticated designs of interaction and aggregation phases effectively boost the perf
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: Evading Ownership Detection Against Deep Learning Modelsry model which moves it decision boundary slightly. Our empirical results demonstrate that the adversary model evaded the DI detection with 40 samples. We also lay out the limitations of MEW and discuss them at last.
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Combining Traffic Assignment and?Traffic Signal Control for?Online Traffic Flow Optimizationthe nearby road network. For traffic signal control, the Maximum Throughput Control (MTC) method is adopted. MTC checks the states of the intersections periodically and greedily takes the action that maximum the throughput of the intersections. By combining these two methods, the vehicle-road coordi
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Frequency Spectrum with?Multi-head Attention for?Face Forgery Detectionrmed our experiments on smaller sets of DFFD dataset and tested on larger sets. The proposed model achieves an accuracy of 99%, having significantly fewer parameters in in-domain settings and thus is computationally less expensive. It is also tested on unseen datasets in cross GANs setting with an a
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