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Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

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31#
發(fā)表于 2025-3-26 21:40:59 | 只看該作者
32#
發(fā)表于 2025-3-27 02:42:32 | 只看該作者
HiCo: Hierarchical Contrastive Learning for?Ultrasound Video Model Pretraininglarities of images between different classes. Experiments with HiCo on five datasets demonstrate its favorable results over state-of-the-art approaches. The source code of this work is publicly available at ..
33#
發(fā)表于 2025-3-27 09:06:33 | 只看該作者
34#
發(fā)表于 2025-3-27 11:28:12 | 只看該作者
Multi-View Coupled Self-Attention Network for?Pulmonary Nodules Classificationon module is proposed to model spatial and dimensional correlations sequentially for learning global spatial contexts and further improving the identification accuracy. Compared with vanilla self-attention, which has three-fold advances: 1) uses less memory consumption and computational complexity t
35#
發(fā)表于 2025-3-27 15:53:16 | 只看該作者
Multi-scale Wavelet Transformer for?Face Forgery Detectionial features. These two attention modules are calculated through a unified transformer block for efficiency. A wide variety of experiments demonstrate that the proposed method is efficient and effective for both within and cross datasets.
36#
發(fā)表于 2025-3-27 19:06:33 | 只看該作者
Improving the?Quality of?Sparse-view Cone-Beam Computed Tomography via?Reconstruction-Friendly Interp a Reconstruction-Friendly Interpolation Network (RFI-Net), which first utilizes a 3D-2D attention network to learn inter-projection relations for synthesizing missing projections, and then introduces a novel Ramp-Filter loss to constrain a frequency consistency between the synthesized and real pro
37#
發(fā)表于 2025-3-28 01:30:13 | 只看該作者
38#
發(fā)表于 2025-3-28 02:58:17 | 只看該作者
39#
發(fā)表于 2025-3-28 07:12:43 | 只看該作者
40#
發(fā)表于 2025-3-28 13:32:16 | 只看該作者
0302-9743 art VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .978-3-031-26350-7978-3-031-26351-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
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