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

道學(xué)氣

Embedded Microelectronic Subsystemsusly diminishes when employed?on image data with blur, while image data with intentional?blur constitute a substantial proportion of general data. To further investigate and address this issue, we developed a?new super-resolution dataset specifically tailored for blur images, named the Real-world Bl

昏暗

Ethics

寬容

Embedded Microelectronic Subsystemss a challenging task.?Many adapter-based methods impose image representation conditions on?the denoising process to accomplish image control. However?these conditions are not aligned with the word embedding space, leading?to interference between image and text control conditions and?the potential lo

micturition

Context in Pervasive Environmentses data from both known and unknown classes. Traditional methods prioritize selecting informative examples with low confidence, with the risk of mistakenly selecting unknown-class examples with similarly low confidence. Recent methods favor the most probable known-class examples, with the risk?of pi

膠水

Ambient Intelligence with Microsystemsion problem. Among various AT methods,?fast AT (FAT), which employs a single-step attack strategy to guide?the training process, can achieve good robustness against adversarial attacks at a low cost. However, FAT methods suffer from?the catastrophic overfitting problem, especially on complex tasks?o

Mangle

遺棄

Immunotherapy

https://doi.org/10.1007/978-3-031-43461-7ication bias and its?loss function design, while ignoring the subtle influence of?the regression branch. This paper shows that the regression bias exists and does adversely and seriously impact the detection accuracy. While existing methods fail to handle the regression bias,?the class-specific regr

發(fā)牢騷

Badr Hirchoua,Brahim Ouhbi,Bouchra Frikhure task, which is in increasing demand, aims to erase objects and generate harmonious background. Previous GAN-based inpainting methods struggle with intricate texture generation. Emerging diffusion model-based algorithms,?such as Stable Diffusion Inpainting, exhibit the capability to generate nove