Palpitation
发表于 2025-3-30 11:31:43
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STALE
发表于 2025-3-30 15:22:32
Peter Kooreman,Sophia Wunderink, given a pre-trained model and its parameters, . enforces edge consistency prior at the inference stage and updates the model based on (a) a single test sample at a time (.), or (b) continuously for the whole test domain (.). Not only the target data, . also does not need access to the source data
策略
发表于 2025-3-30 18:52:13
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收集
发表于 2025-3-30 21:53:49
Growth, Social Innovation and Time Use,e compare against a state-of-the-art regression baseline that uses global image descriptors. Quantitative and qualitative experimental results on the recently proposed VIGOR and the Oxford RobotCar datasets validate our design. The produced probabilities are correlated with localization accuracy, an
perimenopause
发表于 2025-3-31 02:05:48
Steps to Be Taken to Calculate Fair Pricesand OpenCDA. Extensive experimental results demonstrate that V2X-ViT sets new state-of-the-art performance for 3D object detection and achieves robust performance even under harsh, noisy environments. The code is available at ..
gait-cycle
发表于 2025-3-31 05:56:50
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overshadow
发表于 2025-3-31 12:13:50
https://doi.org/10.1007/978-3-030-59166-3les. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian beha
ferment
发表于 2025-3-31 15:41:40
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庇护
发表于 2025-3-31 19:05:50
Chisato Yoshida,Alan D. Woodlandn, we propose deep sparse supervision in the training phase to help convergence and alleviate the memory consumption problem. Our GASN achieves state-of-the-art performance on both SemanticKITTI and Nuscenes datasets while running significantly faster and consuming less memory.
mortgage
发表于 2025-4-1 01:22:34
https://doi.org/10.1057/9780230514881ablish two new large-scale datasets to this field by collecting lidar-scanned point clouds from public autonomous driving datasets and annotating the collected data through novel pseudo-labeling. Extensive experiments on both public and proposed datasets show that our method outperforms prior state-