Zobrazeno 1 - 10
of 2 043
pro vyhledávání: '"Wong, Alex"'
Autor:
Gangopadhyay, Suchisrit, Chen, Xien, Chu, Michael, Rim, Patrick, Park, Hyoungseob, Wong, Alex
We propose UnCLe, a standardized benchmark for Unsupervised Continual Learning of a multimodal depth estimation task: Depth completion aims to infer a dense depth map from a pair of synchronized RGB image and sparse depth map. We benchmark depth comp
Externí odkaz:
http://arxiv.org/abs/2410.18074
Autor:
Zeng, Ziyao, Wu, Yangchao, Park, Hyoungseob, Wang, Daniel, Yang, Fengyu, Soatto, Stefano, Lao, Dong, Hong, Byung-Woo, Wong, Alex
We propose a method for metric-scale monocular depth estimation. Inferring depth from a single image is an ill-posed problem due to the loss of scale from perspective projection during the image formation process. Any scale chosen is a bias, typicall
Externí odkaz:
http://arxiv.org/abs/2410.02924
Autor:
Yang, Fengyu, Feng, Chao, Wang, Daniel, Wang, Tianye, Zeng, Ziyao, Xu, Zhiyang, Park, Hyoungseob, Ji, Pengliang, Zhao, Hanbin, Li, Yuanning, Wong, Alex
Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like electrophysiology and neuroimaging, reflects various aspects of informa
Externí odkaz:
http://arxiv.org/abs/2407.14020
Autor:
Ezhov, Vadim, Park, Hyoungseob, Zhang, Zhaoyang, Upadhyay, Rishi, Zhang, Howard, Chandrappa, Chethan Chinder, Kadambi, Achuta, Ba, Yunhao, Dorsey, Julie, Wong, Alex
We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach, where we take
Externí odkaz:
http://arxiv.org/abs/2405.17315
We describe a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. Since supervised data is often technically impossible to obtain, assumptions and biases have to be imposed to solve this inverse
Externí odkaz:
http://arxiv.org/abs/2405.03662
Autor:
Zeng, Ziyao, Wang, Daniel, Yang, Fengyu, Park, Hyoungseob, Wu, Yangchao, Soatto, Stefano, Hong, Byung-Woo, Lao, Dong, Wong, Alex
Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investi
Externí odkaz:
http://arxiv.org/abs/2404.03635
Autor:
Gella, Blake, Zhang, Howard, Upadhyay, Rishi, Chang, Tiffany, Wei, Nathan, Waliman, Matthew, Ba, Yunhao, de Melo, Celso, Wong, Alex, Kadambi, Achuta
We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions. We begin by examining existing models on images degraded by weather conditions such as rain, fog, or snow, and found that they exhibit a la
Externí odkaz:
http://arxiv.org/abs/2403.14874
Autor:
Zhang, Howard, Ba, Yunhao, Yang, Ethan, Upadhyay, Rishi, Wong, Alex, Kadambi, Achuta, Guo, Yun, Xiao, Xueyao, Wang, Xiaoxiong, Li, Yi, Chang, Yi, Yan, Luxin, Zheng, Chaochao, Wang, Luping, Liu, Bin, Khowaja, Sunder Ali, Yoon, Jiseok, Lee, Ik-Hyun, Zhang, Zhao, Wei, Yanyan, Ren, Jiahuan, Zhao, Suiyi, Zheng, Huan
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023. The aim of this competition is to study the rainy weather phenomenon in real world scenarios, provide a novel real world rainy image
Externí odkaz:
http://arxiv.org/abs/2403.12327
Autor:
Li, Li, Huang, Jia, Wu, Jingsong, Jiang, Cai, Chen, Shanjia, Xie, Guanli, Ren, Jinxin, Tao, Jing, Chan, Chetwyn C H, Chen, Lidian, Wong, Alex W K
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 5, p e17219 (2020)
BackgroundMonitoring the functional status of poststroke patients after they transition home is significant for rehabilitation. Mobile health (mHealth) technologies may provide an opportunity to reach and follow patients post discharge. However, the
Externí odkaz:
https://doaj.org/article/e34282d810014f76a7991924ac33918f
This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs). We present a novel multi-vehicle parking simulator, equipped with hierarchical path planning and collision avoidance capa
Externí odkaz:
http://arxiv.org/abs/2402.14183