Zobrazeno 1 - 10
of 412
pro vyhledávání: '"Wu, Jinjian"'
The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming, especially for authentic images. Training on synthetic data is expected to be beneficial, but synthetically trained models often suffer from poor generaliz
Externí odkaz:
http://arxiv.org/abs/2405.04167
This paper tackles the problem of passive gaze estimation using both event and frame data. Considering the inherently different physiological structures, it is intractable to accurately estimate gaze purely based on a given state. Thus, we reformulat
Externí odkaz:
http://arxiv.org/abs/2404.00548
Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneous
Externí odkaz:
http://arxiv.org/abs/2402.09270
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D neural calibration, a novel pretext task that estimates the rigid pos
Externí odkaz:
http://arxiv.org/abs/2401.12452
Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment) fail to catch them simultaneously. To address the deficiency, current
Externí odkaz:
http://arxiv.org/abs/2401.02614
In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robust and universal object segmentation within the event-centric domain.
Externí odkaz:
http://arxiv.org/abs/2312.16222
Autor:
Fang, Yuqi, Wu, Jinjian, Wang, Qianqian, Qiu, Shijun, Bozoki, Andrea, Yan, Huaicheng, Liu, Mingxia
Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Existing studies usually suffer from significant cross-site/domain data heterogeneity caused by site effects such as differe
Externí odkaz:
http://arxiv.org/abs/2308.12495
Autor:
Zhang, Lintao, Wu, Jinjian, Wang, Lihong, Wang, Li, Steffens, David C., Qiu, Shijun, Potter, Guy G., Liu, Mingxia
Brain structural MRI has been widely used to assess the future progression of cognitive impairment (CI). Previous learning-based studies usually suffer from the issue of small-sized labeled training data, while there exist a huge amount of structural
Externí odkaz:
http://arxiv.org/abs/2306.11837
Autor:
Chen, Xiaoyang, Wu, Jinjian, Lyu, Wenjiao, Zou, Yicheng, Thung, Kim-Han, Liu, Siyuan, Wu, Ye, Ahmad, Sahar, Yap, Pew-Thian
Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associ
Externí odkaz:
http://arxiv.org/abs/2301.01369
In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatial-spectral information of HS images efficiently and effectively. Speci
Externí odkaz:
http://arxiv.org/abs/2205.14887