Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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書(shū)目名稱(chēng)Neural Information Processing影響因子(影響力)




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Neural Information Processing978-981-99-8181-6Series ISSN 1865-0929 Series E-ISSN 1865-0937

暴發(fā)戶(hù)

Communications in Computer and Information Sciencehttp://image.papertrans.cn/n/image/663595.jpg

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公理

Road Meteorological State Recognition in Extreme Weather Based on an Improved Mask-RCNNffic accidents can increase dramatically in winter or during seasonal changes when extreme weather often occurs. To achieve real-time and automatic RSC monitoring, this paper first proposes an improved Mask-RCNN model based on Swin Transformer and path aggregation feature pyramid network (PAFPN) as

Initiative

I-RAFT: Optical Flow Estimation Model Based on?Multi-scale Initialization Strategynt performance improvements. However, existing models that employ recurrent neural networks to update optical flow from an initial value of 0 suffer from issues of instability and slow training. To address this, we propose a simple yet effective optical flow initialization module as part of the opti

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LSiF: Log-Gabor Empowered Siamese Federated Learning for?Efficient Obscene Image Classification in?thole. It is crucial to tackle this problem by implementing efficient content moderation, educating users, and creating technologies and policies that foster a more secure and wholesome online atmosphere. To address this issue, this research proposes the Log-Gabor Empowered Siamese Federated Learning

安定

Depth Normalized Stable View Synthesis supposed to be as close as possible to the scene content. We present Deep Normalized Stable View Synthesis (DNSVS), an NVS method for large-scale scenes based on the pipeline of Stable View Synthesis (SVS). SVS combines neural networks with the 3D scene representation obtained from structure-from-m

Memorial

Exploring the?Integration of?Large Language Models into?Automatic Speech Recognition Systems: An Emp The increasing sophistication of LLMs, with their in-context learning capabilities and instruction-following behavior, has drawn significant attention in the field of Natural Language Processing (NLP). Our primary focus is to investigate the potential of using an LLM’s in-context learning capabilit