pus840
发表于 2025-3-30 08:39:30
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颠簸下上
发表于 2025-3-30 15:42:24
Learning Progressive Joint Propagation for Human Motion Prediction,g data to guide the predictions. We evaluate the proposed method on two challenging benchmark datasets (Human3.6M and CMU-Mocap). Experimental results show our superior performance compared with the state-of-the-art approaches.
hemorrhage
发表于 2025-3-30 16:36:13
The Group Loss for Deep Metric Learning, neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We show state-of-the-art results on clustering and image retrieval on several datasets, and show the potential of our method when combined with other techniques such as ensembles. To facilitat
Hormones
发表于 2025-3-30 22:12:40
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似少年
发表于 2025-3-31 04:17:30
https://doi.org/10.1007/978-3-540-27980-8ural networks, MVDet takes a fully convolutional approach with large convolutional kernels on the multiview aggregated feature map. The proposed model is end-to-end learnable and achieves 88.2% MODA on Wildtrack dataset, outperforming the state-of-the-art by 14.1%. We also provide detailed analysis
脱离
发表于 2025-3-31 05:09:53
,Magnetic Signature of the Earth’s Crust,n in unseen environment, is applied in testing. Experiment in the artificial environment AI2-Thor validates that each of the techniques is effective. When combined, the techniques bring significantly improvement over baseline methods in navigation effectiveness and efficiency in unseen environments.
种植,培养
发表于 2025-3-31 10:40:05
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有法律效应
发表于 2025-3-31 13:52:04
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共栖
发表于 2025-3-31 19:37:10
https://doi.org/10.1007/978-3-540-27980-8y given priorities to condition on the generator side, not on the discriminator side of GANs. We apply the conditions on the discriminator side as well via multi-task learning. We enhanced four state-of-the-art cGANs architectures: Stargan, Stargan-JNT, AttGAN and STGAN. Our extensive qualitative an
美学
发表于 2025-3-31 23:25:41
,Magnetic Signature of the Earth’s Crust, where the model has access to the full input. The proposed method outperforms current state-of-the-art on unsupervised image segmentation. It is simple and easy to implement, and can be extended to other visual tasks and integrated seamlessly into existing unsupervised learning methods requiring di