essential-fats
发表于 2025-3-23 13:01:06
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改变
发表于 2025-3-23 15:51:07
https://doi.org/10.1007/978-1-4302-4102-7nformation by widening the receptive field. The extensive analysis of the proposed method is carried out by considering benchmark synthetic hazy video databases and analyzed quantitative results. Experimental results show that the proposed method out performs the other state-of-the-art (SOTA) existi
acrobat
发表于 2025-3-23 21:37:09
Balasubramanian Raman,Subrahmanyam Murala,Puneet G
harmony
发表于 2025-3-24 01:57:48
,Handwritten Text Retrieval from Unlabeled Collections,nstraint and huge collection size. Our framework allows the addition of new collections without any need for specific finetuning or labeling. Finally, we also present a demonstration of the retrieval framework. ..
pineal-gland
发表于 2025-3-24 03:14:38
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清真寺
发表于 2025-3-24 10:20:20
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CRAFT
发表于 2025-3-24 14:05:28
,Comparative Analysis of Machine Learning and Deep Learning Models for Ship Classification from Sateion carried out reveals that traditional machine learning performs well when trained and tested on a single dataset. However, there is a drastic change in the performance of machine learning models when tested on a different ship dataset. The results show that the deep learning models have better fe
micronized
发表于 2025-3-24 16:15:54
,Channel Difference Based Regeneration Architecture for Fake Colorized Image Detection,dge, this is the first work with CDM based auto-encoder for FCID. The performance of the proposed network is tested on benchmark datasets and compared with the existing state-of-the-art methods for FCID in terms of half total error rate (HTER). The experimental results demonstrate that the proposed
婚姻生活
发表于 2025-3-24 20:44:39
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浮夸
发表于 2025-3-25 02:38:35
MFCA-Net: Multiscale Feature Fusion with Channel-Wise Attention Network for Automatic Liver Segmentultiscale information at a more granular level..Further, we reconstructed the multiscale low-level features and fused them with high-level features that enhance the semantic details in the features. In addition, we employed the channel-wise attention mechanism that renovates the features by modellin