Zobrazeno 1 - 10
of 54 819
pro vyhledávání: '"editable"'
Autor:
Su, Xin, Zheng, Zhuoran
With the rising imaging resolution of handheld devices, existing multi-exposure image fusion algorithms struggle to generate a high dynamic range image with ultra-high resolution in real-time. Apart from that, there is a trend to design a manageable
Externí odkaz:
http://arxiv.org/abs/2412.13749
Real-time computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-oriented characteristics of conventional communications often do not align
Externí odkaz:
http://arxiv.org/abs/2411.15702
Brain tumor segmentation is crucial for accurate diagnosisand treatment planning, but the small size and irregular shapeof tumors pose significant challenges. Existing methods of-ten fail to effectively incorporate medical domain knowledgesuch as tum
Externí odkaz:
http://arxiv.org/abs/2410.19847
Autor:
Jiang, Zhimeng, Liu, Zirui, Han, Xiaotian, Feng, Qizhang, Jin, Hongye, Tan, Qiaoyu, Zhou, Kaixiong, Zou, Na, Hu, Xia
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks can make prediction errors after deployment as the world changes. \te
Externí odkaz:
http://arxiv.org/abs/2410.15556
Autor:
Szymkowiak, Jakub, Jakubowska, Weronika, Malarz, Dawid, Smolak-Dyżewska, Weronika, Zięba, Maciej, Musialski, Przemysław, Pałubicki, Wojtek, Spurek, Przemysław
In computer graphics, there is a need to recover easily modifiable representations of 3D geometry and appearance from image data. We introduce a novel method for this task using 3D Gaussian Splatting, which enables intuitive scene editing through mes
Externí odkaz:
http://arxiv.org/abs/2411.18311
Recently, with the development of Neural Radiance Fields and Gaussian Splatting, 3D reconstruction techniques have achieved remarkably high fidelity. However, the latent representations learnt by these methods are highly entangled and lack interpreta
Externí odkaz:
http://arxiv.org/abs/2410.01535
Autor:
Waczyńska, Joanna, Szczepanik, Tomasz, Borycki, Piotr, Tadeja, Sławomir, Bohné, Thomas, Spurek, Przemysław
Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes,
Externí odkaz:
http://arxiv.org/abs/2410.01521
In the age of mobile internet, user data, often referred to as memories, is continuously generated on personal devices. Effectively managing and utilizing this data to deliver services to users is a compelling research topic. In this paper, we introd
Externí odkaz:
http://arxiv.org/abs/2409.19401
Generating fair and accurate predictions plays a pivotal role in deploying large language models (LLMs) in the real world. However, existing debiasing methods inevitably generate unfair or incorrect predictions as they are designed and evaluated to a
Externí odkaz:
http://arxiv.org/abs/2408.11843
The area of portrait image animation, propelled by audio input, has witnessed notable progress in the generation of lifelike and dynamic portraits. Conventional methods are limited to utilizing either audios or facial key points to drive images into
Externí odkaz:
http://arxiv.org/abs/2407.08136