Concerto
发表于 2025-3-23 12:54:37
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珍奇
发表于 2025-3-23 16:03:56
Learning High-Performance Spiking Neural Networks with Multi-Compartment Spiking Neuronsd improve the performance of SNNs. Besides, we design the Binarized Synaptic Encoder (BSE) to reduce the computation cost for the input of SNNs. Experimental results show that the MC-SNN performs well on the neuromorphic datasets, reaching 79.52% and 81.24% on CIFAR10-DVS and N-Caltech101, respectiv
diabetes
发表于 2025-3-23 21:08:36
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Exposition
发表于 2025-3-24 01:11:54
Behavioural State Detection Algorithm for Infants and Toddlers Incorporating Multi-scale Contextual al structure and dilated convolution. The experimental results show that the method achieves a detection speed of 72.18 FPS and a detection accuracy of 95.24%, which enables faster detection of infants and toddlers’ behavioural states and slightly better accuracy relative to the baseline algorithm.
collateral
发表于 2025-3-24 02:21:10
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmarkuch distinctive architecture, the dual-branch feature flow information is interacted and compensated in a decomposition-then-fusion manner. Moreover, we demonstrate significant performance improvements of the proposed . framework on different difficulty-level tasks. We also implement long-term discr
escalate
发表于 2025-3-24 07:40:13
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让你明白
发表于 2025-3-24 12:17:40
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过去分词
发表于 2025-3-24 15:48:14
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调整校对
发表于 2025-3-24 19:25:21
DensityLayout: Density-Conditioned Layout GAN for Visual-Textual Presentation Designsnerator conditioned on these visual features will generate preliminary layouts. Finally, a . illustrating the inclusion relationships between elements is presented, and a graph convolution network will fine-tune the layouts. The effectiveness of the proposed approach is validated on CGL-Dataset, sho
Mobile
发表于 2025-3-25 00:00:21
GLTCM: Global-Local Temporal and Cross-Modal Network for Audio-Visual Event Localization information of multi-modal features, and the localization module is based on multi-task learning. Our proposed method is verified for two tasks of supervised and weakly-supervised audio-visual event localization. The experimental results demonstrated that our method is competitive on the public AVE