JADE
发表于 2025-3-21 18:16:32
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值得赞赏
发表于 2025-3-21 21:55:18
Neural Information Processing978-981-99-8181-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
暴发户
发表于 2025-3-22 02:34:49
Communications in Computer and Information Sciencehttp://image.papertrans.cn/n/image/663595.jpg
Medicare
发表于 2025-3-22 05:22:49
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公理
发表于 2025-3-22 11:54:47
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
发表于 2025-3-22 13:36:54
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
ENDOW
发表于 2025-3-22 18:26:49
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nonplus
发表于 2025-3-22 22:38:47
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
发表于 2025-3-23 05:01:32
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
发表于 2025-3-23 08:59:59
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