Zobrazeno 1 - 10
of 4 773
pro vyhledávání: '"An, Zixiao"'
In the field of automated programming, large language models (LLMs) have demonstrated foundational generative capabilities when given detailed task descriptions. However, their current functionalities are primarily limited to function-level developme
Externí odkaz:
http://arxiv.org/abs/2410.19245
Autor:
Ding, Hongcheng, Hu, Fuzhen, Zhao, Xuanze, Jiang, Zixiao, Abdullah, Shamsul Nahar, Dewi, Deshinta Arrova
Sentiment analysis has become increasingly important for assessing public opinion and informing decision-making. Large language models (LLMs) have revolutionized this field by capturing nuanced language patterns. However, adapting LLMs to domain-spec
Externí odkaz:
http://arxiv.org/abs/2410.16589
The traditional image inpainting task aims to restore corrupted regions by referencing surrounding background and foreground. However, the object erasure task, which is in increasing demand, aims to erase objects and generate harmonious background. P
Externí odkaz:
http://arxiv.org/abs/2410.10207
Autor:
Zhao, ZiXiao, Fard, Fatemeh H.
Recent advancements in developing Pre-trained Language Models for Code (Code-PLMs) have urged many areas of Software Engineering (SE) and brought breakthrough results for many SE tasks. Though these models have achieved the state-of-the-art performan
Externí odkaz:
http://arxiv.org/abs/2410.07793
Existing scene text removal (STR) task suffers from insufficient training data due to the expensive pixel-level labeling. In this paper, we aim to address this issue by introducing a Text-aware Masked Image Modeling algorithm (TMIM), which can pretra
Externí odkaz:
http://arxiv.org/abs/2409.13431
Accurate forecasting of the EUR/USD exchange rate is crucial for investors, businesses, and policymakers. This paper proposes a novel framework, IUS, that integrates unstructured textual data from news and analysis with structured data on exchange ra
Externí odkaz:
http://arxiv.org/abs/2408.13214
Autor:
Wang, Zixiao, Fan, Jicong
Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kern
Externí odkaz:
http://arxiv.org/abs/2408.11370
Autor:
Ding, Hongcheng, Zhao, Xuanze, Abdullah, Shamsul Nahar, Dewi, Deshinta Arrova, Jiang, Zixiao, Shi, Xiangyu
Sentiment analysis plays a crucial role in various domains, such as business intelligence and financial forecasting. Large language models (LLMs) have become a popular paradigm for sentiment analysis, leveraging multi-task learning to address specifi
Externí odkaz:
http://arxiv.org/abs/2408.11856
Autor:
He, Qianyun, Ji, Xinya, Gong, Yicheng, Lu, Yuanxun, Diao, Zhengyu, Huang, Linjia, Yao, Yao, Zhu, Siyu, Ma, Zhan, Xu, Songcen, Wu, Xiaofei, Zhang, Zixiao, Cao, Xun, Zhu, Hao
We present a novel approach for synthesizing 3D talking heads with controllable emotion, featuring enhanced lip synchronization and rendering quality. Despite significant progress in the field, prior methods still suffer from multi-view consistency a
Externí odkaz:
http://arxiv.org/abs/2408.00297
Imagine there is a disruption in train 1 near Times Square metro station. You try to find an alternative subway route to the JFK airport on Google Maps, but the app fails to provide a suitable recommendation that takes into account the disruption and
Externí odkaz:
http://arxiv.org/abs/2407.14926