Zobrazeno 1 - 10
of 415
pro vyhledávání: '"Cai, Hongmin"'
The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is
Externí odkaz:
http://arxiv.org/abs/2402.19147
Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image
We tackle the problem of single-image Human Mesh Recovery (HMR). Previous approaches are mostly based on a single crop. In this paper, we shift the single-crop HMR to a novel multiple-crop HMR paradigm. Cropping a human from image multiple times by s
Externí odkaz:
http://arxiv.org/abs/2402.02074
Without human annotations, a typical Unsupervised Video Anomaly Detection (UVAD) method needs to train two models that generate pseudo labels for each other. In previous work, the two models are closely entangled with each other, and it is not known
Externí odkaz:
http://arxiv.org/abs/2401.13551
Autor:
Liu, Zhengliang, Holmes, Jason, Liao, Wenxiong, Liu, Chenbin, Zhang, Lian, Feng, Hongying, Wang, Peilong, Elahi, Muhammad Ali, Cai, Hongmin, Sun, Lichao, Li, Quanzheng, Li, Xiang, Liu, Tianming, Shen, Jiajian, Liu, Wei
We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology, an important medical specialty that has received limited attention from the NLP community in the past. With t
Externí odkaz:
http://arxiv.org/abs/2401.10995
In recent years, tensor networks have emerged as powerful tools for solving large-scale optimization problems. One of the most promising tensor networks is the tensor ring (TR) decomposition, which achieves circular dimensional permutation invariance
Externí odkaz:
http://arxiv.org/abs/2307.10620
Autor:
Cai, Hongmin, Huang, Xiaoke, Liu, Zhengliang, Liao, Wenxiong, Dai, Haixing, Wu, Zihao, Zhu, Dajiang, Ren, Hui, Li, Quanzheng, Liu, Tianming, Li, Xiang
Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease. This study inv
Externí odkaz:
http://arxiv.org/abs/2307.02514
Autor:
Liao, Wenxiong, Liu, Zhengliang, Dai, Haixing, Xu, Shaochen, Wu, Zihao, Zhang, Yiyang, Huang, Xiaoke, Zhu, Dajiang, Cai, Hongmin, Liu, Tianming, Li, Xiang
Background: Large language models such as ChatGPT are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the Internet. However, medical texts such as clinical notes
Externí odkaz:
http://arxiv.org/abs/2304.11567
Autor:
Dai, Haixing, Liu, Zhengliang, Liao, Wenxiong, Huang, Xiaoke, Cao, Yihan, Wu, Zihao, Zhao, Lin, Xu, Shaochen, Liu, Wei, Liu, Ninghao, Li, Sheng, Zhu, Dajiang, Cai, Hongmin, Sun, Lichao, Li, Quanzheng, Shen, Dinggang, Liu, Tianming, Li, Xiang
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning scenario, where the data in the targ
Externí odkaz:
http://arxiv.org/abs/2302.13007
Autor:
Liao, Wenxiong, Liu, Zhengliang, Dai, Haixing, Wu, Zihao, Zhang, Yiyang, Huang, Xiaoke, Chen, Yuzhong, Jiang, Xi, Liu, Wei, Zhu, Dajiang, Liu, Tianming, Li, Sheng, Li, Xiang, Cai, Hongmin
Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot lear
Externí odkaz:
http://arxiv.org/abs/2302.10447
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, resulting in inaccuracy to encode the sample proximity and suboptimal perfor
Externí odkaz:
http://arxiv.org/abs/2302.01569