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Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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樓主: Coolidge
11#
發(fā)表于 2025-3-23 10:44:23 | 只看該作者
https://doi.org/10.1007/978-3-642-85538-2Both neural networks are trained using an objective formulated with the aid of self-supervised monocular SLAM on a collection of underwater videos. Thus, our method does not requires any ground-truth color images or caustics labels, and corrects images in real-time. We experimentally demonstrate the
12#
發(fā)表于 2025-3-23 13:52:02 | 只看該作者
https://doi.org/10.1007/978-3-642-85538-2nal datasets, COSTG incorporates not only standard semantic maps but also some textual descriptions of curvilinear object features. To ensure consistency between synthetic semantic maps and images, we introduce the Semantic Consistency Preserving ControlNet (SCP ControlNet). This involves an adaptat
13#
發(fā)表于 2025-3-23 19:41:46 | 只看該作者
https://doi.org/10.1007/978-981-99-3451-5se distributions to their globally balanced and entropy regularized version, which is obtained through a simple self-optimal-transport computation. We ablate and verify our method through a wide set of experiments that show competitive performance with leading methods on both semi-supervised and tra
14#
發(fā)表于 2025-3-24 00:13:55 | 只看該作者
15#
發(fā)表于 2025-3-24 03:02:13 | 只看該作者
16#
發(fā)表于 2025-3-24 09:40:27 | 只看該作者
,Few-Shot Image Generation by?Conditional Relaxing Diffusion Inversion, scheduler that progressively introduces perturbations to the SGE, thereby augmenting diversity. Comprehensive experiments demonstrate that our method outperforms GAN-based reconstruction techniques and achieves comparable performance to state-of-the-art (SOTA) FSIG methods. Additionally, it effecti
17#
發(fā)表于 2025-3-24 11:08:49 | 只看該作者
Data Poisoning Quantization Backdoor Attack,model. The key component is a trigger pattern generator, which is trained together with a surrogate model in an alternating manner. The attack’s effectiveness is tested on multiple benchmark datasets, including CIFAR10, CelebA, and ImageNet10, as well as state-of-the-art backdoor defenses.
18#
發(fā)表于 2025-3-24 14:54:16 | 只看該作者
,DailyDVS-200: A Comprehensive Benchmark Dataset for?Event-Based Action Recognition,equences. This dataset is designed to reflect a broad spectrum of action types, scene complexities, and data acquisition diversity. Each sequence in the dataset is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions. Moreover, DailyDVS-200 is structured to faci
19#
發(fā)表于 2025-3-24 19:27:12 | 只看該作者
20#
發(fā)表于 2025-3-25 01:42:07 | 只看該作者
,T-CorresNet: Template Guided 3D Point Cloud Completion with?Correspondence Pooling Query Generation the complete point proxies. Finally, we generate the complete point cloud with a FoldingNet following the coarse-to-fine paradigm, according to the fine template and the predicted point proxies. Experimental results demonstrate that our T-CorresNet outperforms the state-of-the-art methods on severa
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