Zobrazeno 1 - 10
of 75 063
pro vyhledávání: '"AN Xiaomin"'
Autor:
Li, Xiaomin, Jia, Xu, Wang, Qinghe, Diao, Haiwen, Ge, Mengmeng, Li, Pengxiang, He, You, Lu, Huchuan
Existing pretrained text-to-video (T2V) models have demonstrated impressive abilities in generating realistic videos with basic motion or camera movement. However, these models exhibit significant limitations when generating intricate, human-centric
Externí odkaz:
http://arxiv.org/abs/2412.01343
Autor:
Li, Xiaomin, Sha, Junyi
Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sale
Externí odkaz:
http://arxiv.org/abs/2411.14737
In affective computing, the task of Emotion Recognition in Conversations (ERC) has emerged as a focal area of research. The primary objective of this task is to predict emotional states within conversations by analyzing multimodal data including text
Externí odkaz:
http://arxiv.org/abs/2411.13917
Autor:
Ouyang, Xiaomin, Wu, Jason, Kimura, Tomoyoshi, Lin, Yihan, Verma, Gunjan, Abdelzaher, Tarek, Srivastava, Mani
Multimodal sensing systems are increasingly prevalent in various real-world applications. Most existing multimodal learning approaches heavily rely on training with a large amount of complete multimodal data. However, such a setting is impractical in
Externí odkaz:
http://arxiv.org/abs/2411.12126
Messenger RNA (mRNA) vaccines and therapeutics are emerging as powerful tools against a variety of diseases, including infectious diseases and cancer. The design of mRNA molecules, particularly the untranslated region (UTR) and coding sequence (CDS)
Externí odkaz:
http://arxiv.org/abs/2410.20781
Autor:
Zhang, Zhiwei, Wang, Fali, Li, Xiaomin, Wu, Zongyu, Tang, Xianfeng, Liu, Hui, He, Qi, Yin, Wenpeng, Wang, Suhang
Large language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora. However, LLMs may also acquire unwanted behaviors from the diverse and sensitive nature of their training d
Externí odkaz:
http://arxiv.org/abs/2410.16454
Autor:
Quan, Pengrui, Ouyang, Xiaomin, Jeyakumar, Jeya Vikranth, Wang, Ziqi, Xing, Yang, Srivastava, Mani
Effective processing, interpretation, and management of sensor data have emerged as a critical component of cyber-physical systems. Traditionally, processing sensor data requires profound theoretical knowledge and proficiency in signal-processing too
Externí odkaz:
http://arxiv.org/abs/2410.10741
Autor:
Song, Zhiyun, Zhao, Yinjie, Li, Xiaomin, Fei, Manman, Zhao, Xiangyu, Liu, Mengjun, Chen, Cunjian, Yeh, Chung-Hsing, Wang, Qian, Zheng, Guoyan, Ai, Songtao, Zhang, Lichi
High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various medical image analysis tasks. Due to the high demands of acquisition dev
Externí odkaz:
http://arxiv.org/abs/2410.10097
The quality of training data significantly impacts the performance of large language models (LLMs). There are increasing studies using LLMs to rate and select data based on several human-crafted metrics (rules). However, these conventional rule-based
Externí odkaz:
http://arxiv.org/abs/2410.04715
We design and demonstrate the first field trial of LLM-powered AI Agent for ADON. Three operation modes of the Agent are proposed for network lifecycle management. The Agent efficiently processes wavelength add/drop and soft/hard failures, and achiev
Externí odkaz:
http://arxiv.org/abs/2409.14605