Zobrazeno 1 - 10
of 9 177
pro vyhledávání: '"Chan, S. A."'
Autor:
Hu, Dongting, Chen, Jierun, Huang, Xijie, Coskun, Huseyin, Sahni, Arpit, Gupta, Aarush, Goyal, Anujraaj, Lahiri, Dishani, Singh, Rajesh, Idelbayev, Yerlan, Cao, Junli, Li, Yanyu, Cheng, Kwang-Ting, Chan, S. -H. Gary, Gong, Mingming, Tulyakov, Sergey, Kag, Anil, Xu, Yanwu, Ren, Jian
Existing text-to-image (T2I) diffusion models face several limitations, including large model sizes, slow runtime, and low-quality generation on mobile devices. This paper aims to address all of these challenges by developing an extremely small and f
Externí odkaz:
http://arxiv.org/abs/2412.09619
Autor:
Yu, Zhongyi, Wu, Zhenghao, Zhong, Shuhan, Su, Weifeng, Chan, S. -H. Gary, Lee, Chul-Ho, Zhuo, Weipeng
Missing values are a common problem that poses significant challenges to data analysis and machine learning. This problem necessitates the development of an effective imputation method to fill in the missing values accurately, thereby enhancing the o
Externí odkaz:
http://arxiv.org/abs/2410.08794
Autor:
Chen, Jierun, Wei, Fangyun, Zhao, Jinjing, Song, Sizhe, Wu, Bohuai, Peng, Zhuoxuan, Chan, S. -H. Gary, Zhang, Hongyang
Referring expression comprehension (REC) involves localizing a target instance based on a textual description. Recent advancements in REC have been driven by large multimodal models (LMMs) like CogVLM, which achieved 92.44% accuracy on RefCOCO. Howev
Externí odkaz:
http://arxiv.org/abs/2406.16866
Knowing a pedestrian's conveyor state of "elevator," "escalator," or "neither" is fundamental in many applications such as indoor navigation and people flow management. We study, for the first time, classifying the conveyor state of a pedestrian, giv
Externí odkaz:
http://arxiv.org/abs/2405.03218
Autor:
Sri, S Deepika, S, Mohammed Aadil, R, Sanjjushri Varshini, Raman, Raja CSP, Rajagopal, Gopinath, Chan, S Taranath
In the contemporary landscape of technological advancements, the automation of manual processes is crucial, compelling the demand for huge datasets to effectively train and test machines. This research paper is dedicated to the exploration and implem
Externí odkaz:
http://arxiv.org/abs/2404.10678
Autor:
Peng, Zhuoxuan, Chan, S. -H. Gary
Due to its promising results, density map regression has been widely employed for image-based crowd counting. The approach, however, often suffers from severe performance degradation when tested on data from unseen scenarios, the so-called "domain sh
Externí odkaz:
http://arxiv.org/abs/2403.09124
Publikováno v:
Journal of Asian Sociology, 2024 Jun 01. 53(2), 127-149.
Externí odkaz:
https://www.jstor.org/stable/27320233
Knowledge distillation (KD) has been recognized as an effective tool to compress and accelerate models. However, current KD approaches generally suffer from an accuracy drop and/or an excruciatingly long distillation process. In this paper, we tackle
Externí odkaz:
http://arxiv.org/abs/2312.13223
Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between the target and source using unlabeled target data. Source-free UDA removes the requirement for labeled source data at the target to preserve data privacy and storage. However
Externí odkaz:
http://arxiv.org/abs/2312.00540
Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing
Externí odkaz:
http://arxiv.org/abs/2310.11959