Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Ba, Yunhao"'
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
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:
Gella, Blake, Zhang, Howard, Upadhyay, Rishi, Chang, Tiffany, Waliman, Matthew, Ba, Yunhao, Wong, Alex, Kadambi, Achuta
The introduction of large, foundational models to computer vision has led to drastically improved performance on the task of semantic segmentation. However, these existing methods exhibit a large performance drop when testing on images degraded by we
Externí odkaz:
http://arxiv.org/abs/2312.09534
Autor:
Upadhyay, Rishi, Zhang, Howard, Ba, Yunhao, Yang, Ethan, Gella, Blake, Jiang, Sicheng, Wong, Alex, Kadambi, Achuta
While perspective is a well-studied topic in art, it is generally taken for granted in images. However, for the recent wave of high-quality image synthesis methods such as latent diffusion models, perspective accuracy is not an explicit requirement.
Externí odkaz:
http://arxiv.org/abs/2312.00944
Several papers have rightly included minority groups in artificial intelligence (AI) training data to improve test inference for minority groups and/or society-at-large. A society-at-large consists of both minority and majority stakeholders. A common
Externí odkaz:
http://arxiv.org/abs/2209.00746
Autor:
Ba, Yunhao, Zhang, Howard, Yang, Ethan, Suzuki, Akira, Pfahnl, Arnold, Chandrappa, Chethan Chinder, de Melo, Celso, You, Suya, Soatto, Stefano, Wong, Alex, Kadambi, Achuta
We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-a
Externí odkaz:
http://arxiv.org/abs/2206.10779
Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e.g., heart rate) using facial videos. Recent deep learning architectures have improved the accuracy of such physiological measurement signif
Externí odkaz:
http://arxiv.org/abs/2106.06007
In this paper, we teach a machine to discover the laws of physics from video streams. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing
Externí odkaz:
http://arxiv.org/abs/1911.11893
Machine learning in context of physical systems merits a re-examination of the learning strategy. In addition to data, one can leverage a vast library of physical prior models (e.g. kinematics, fluid flow, etc) to perform more robust inference. The n
Externí odkaz:
http://arxiv.org/abs/1910.00201