cinder
发表于 2025-3-25 06:01:15
Régis Bourbonnais,Sophie Méritet the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches. We finally showcase . as a standalone module for unsupervised image partition
热烈的欢迎
发表于 2025-3-25 08:03:44
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zonules
发表于 2025-3-25 12:54:45
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全面
发表于 2025-3-25 18:07:29
Models with Endogenous Regressors,ts of the same person. ZoomNet is able to significantly outperform existing methods on the proposed COCO-WholeBody dataset. Extensive experiments show that COCO-WholeBody not only can be used to train deep models from scratch for whole-body pose estimation but also can serve as a powerful pre-traini
Measured
发表于 2025-3-25 20:27:16
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Mindfulness
发表于 2025-3-26 01:40:39
Bo Honoré,Francis Vella,Marno Verbeekas a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at ..
厌食症
发表于 2025-3-26 04:50:10
Unit Roots and Cointegration in Panelso spotting individual points in the parametric domain, making the post-processing steps, .non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features, that are critical to accurate line detection. Experimental results on a public dataset demonst
结合
发表于 2025-3-26 08:36:12
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信条
发表于 2025-3-26 14:17:36
Panel Data with Measurement Errorsmpractical in many realistic applications..To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed network is able to learn the 3D body shape and pose acr
nocturnal
发表于 2025-3-26 19:35:36
Hashem Pesaran,Ron Smith,Kyung So Imdictive power of specialized features while retaining the universal applicability of domain-invariant features. We demonstrate competitive performance compared to naive baselines and state-of-the-art methods on both PACS and DomainNet (Our code is available at .).