Barrister
发表于 2025-3-28 15:17:37
http://reply.papertrans.cn/25/2424/242350/242350_41.png
整洁
发表于 2025-3-28 22:14:51
http://reply.papertrans.cn/25/2424/242350/242350_42.png
可卡
发表于 2025-3-29 02:38:10
CMOS Image Sensors for Ambient Intelligencessing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estim
Ballerina
发表于 2025-3-29 04:00:20
The Physical Basis of Ambient Intelligence methods address this issue by synthesizing anomalies with noise or external data. However, there is always a large semantic gap between synthetic and real-world anomalies, resulting in weak performance in anomaly detection. To solve the problem, we propose a few-shot Anomaly-driven Generation (AnoG
sultry
发表于 2025-3-29 09:50:34
http://reply.papertrans.cn/25/2424/242350/242350_45.png
Semblance
发表于 2025-3-29 15:21:05
Melanie Walker,Elaine Unterhalterced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual facial styles. To address these issues, we propose EMO, a novel framework that utilizes a
繁荣中国
发表于 2025-3-29 16:20:07
http://reply.papertrans.cn/25/2424/242350/242350_47.png
得罪
发表于 2025-3-29 21:21:20
Luisa S. Deprez,Sandra S. Butlered on the model training phase. However, these approaches become impractical when dealing with the outsourcing of sensitive data. Furthermore, they have encountered significant challenges in balancing the utility-privacy trade-off. How can we generate privacy-preserving surrogate data suitable for u
忍受
发表于 2025-3-30 01:09:20
http://reply.papertrans.cn/25/2424/242350/242350_49.png
Agronomy
发表于 2025-3-30 04:30:28
Building a High-Contrast Planetary Newtonianribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce