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

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书目名称Computer Vision – ECCV 2024影响因子(影响力)




书目名称Computer Vision – ECCV 2024影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2024网络公开度




书目名称Computer Vision – ECCV 2024网络公开度学科排名




书目名称Computer Vision – ECCV 2024被引频次




书目名称Computer Vision – ECCV 2024被引频次学科排名




书目名称Computer Vision – ECCV 2024年度引用




书目名称Computer Vision – ECCV 2024年度引用学科排名




书目名称Computer Vision – ECCV 2024读者反馈




书目名称Computer Vision – ECCV 2024读者反馈学科排名

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欺骗世家

Data Collection-Free Masked Video Modeling,cerns related to privacy, licensing, and inherent biases. Synthesizing data is one of the promising ways to solve these issues, yet pre-training solely on synthetic data has its own challenges. In this paper, we introduce an effective self-supervised learning framework for videos that leverages read

人类学家

,Protecting NeRFs’ Copyright via Plug-And-Play Watermarking Base Model,s intellectual property has become increasingly important. In this paper, we propose ., which adopts a plug-and-play strategy to protect NeRF’s copyright during its creation. NeRFProtector utilizes a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while

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,Pixel-Aware Stable Diffusion for Realistic Image Super-Resolution and Personalized Stylization,pre-trained text-to-image stable diffusion models provide a potential solution to the challenging realistic image super-resolution (Real-ISR) and image stylization problems with their strong generative priors. However, the existing methods along this line often fail to keep faithful pixel-wise image

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情节剧

,AEDNet: Adaptive Embedding and Multiview-Aware Disentanglement for Point Cloud Completion,l structure of the object and reconstructs local details. To this end, we propose a global perception and local attention network, termed AEDNet, for point cloud completion. The proposed AEDNet utilizes designed adaptive point cloud embedding and disentanglement (AED) module in both the encoder and

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,Synergy of Sight and Semantics: Visual Intention Understanding with CLIP,esource-intensive annotation process. Current leading approaches are held back by the limited amount of labeled data. To mitigate the scarcity of annotated data, we leverage the Contrastive Language-Image Pre-training (CLIP) model, renowned for its wealth knowledge in textual and visual modalities.

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