no code implementations • 2 May 2024 • Seungyeop Lee, Knut Peterson, Solmaz Arezoomandan, Bill Cai, Peihan Li, Lifeng Zhou, David Han
A major obstacle to the development of effective monocular depth estimation algorithms is the difficulty in obtaining high-quality depth data that corresponds to collected RGB images.
no code implementations • 9 Apr 2024 • Bill Cai, Clarence Boon Liang Ng, Daniel Tan, Shelvia Hotama
We introduce a new automatic evaluation metric called OxfordEval that measures the win-rate of generated sentences against existing Oxford Dictionary sentences.
1 code implementation • 6 Jun 2021 • John Cai, Bill Cai, ShengMei Shen
In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL), a novel, versatile and highly effective solution that delivers significant out-performance over state-of-the-art methods for cross-domain few-shot learning.
Ranked #1 on Cross-Domain Few-Shot on miniImagenet
no code implementations • 3 Dec 2020 • John Cai, Bill Cai, Sheng Mei Shen
Our method, called Score-based Meta Transfer-Learning (SB-MTL), combines transfer-learning and meta-learning by using a MAML-optimized feature encoder and a score-based Graph Neural Network.
1 code implementation • 3 Dec 2019 • Bill Cai, Xiaojiang Li, Carlo Ratti
Urban canopy cover is important to mitigate the impact of climate change.
no code implementations • 22 Jan 2019 • Zhoutong Wang, Qianhui Liang, Fabio Duarte, Fan Zhang, Louis Charron, Lenna Johnsen, Bill Cai, Carlo Ratti
Evaluating legibility is particularly desirable in indoor spaces, since it has a large impact on human behavior and the efficiency of space utilization.
no code implementations • 3 Dec 2018 • Yuji Yoshimura, Bill Cai, Zhoutong Wang, Carlo Ratti
Our clustering of architectural designs remarkably corroborates conventional views in architectural history, and the learned architectural features also coheres with the traditional understanding of architectural designs.