Zobrazeno 1 - 10
of 919
pro vyhledávání: '"Ye, Jieping"'
This paper proposes a new effective and efficient plug-and-play backbone for video-based person re-identification (ReID). Conventional video-based ReID methods typically use CNN or transformer backbones to extract deep features for every position in
Externí odkaz:
http://arxiv.org/abs/2501.16811
Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g.,
Externí odkaz:
http://arxiv.org/abs/2501.04941
Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-context learning of closed-source LLMs (e.g., GPT-4), which limits their ap
Externí odkaz:
http://arxiv.org/abs/2412.10138
Autor:
Lin, Jingyu, Gu, Jiaqi, Fan, Lubin, Wu, Bojian, Lou, Yujing, Chen, Renjie, Liu, Ligang, Ye, Jieping
Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image and m
Externí odkaz:
http://arxiv.org/abs/2412.03844
Autor:
Zhang, Xiaofeng, Quan, Yihao, Gu, Chaochen, Shen, Chen, Yuan, Xiaosong, Yan, Shaotian, Cheng, Hao, Wu, Kaijie, Ye, Jieping
The hallucination problem in multimodal large language models (MLLMs) remains a common issue. Although image tokens occupy a majority of the input sequence of MLLMs, there is limited research to explore the relationship between image tokens and hallu
Externí odkaz:
http://arxiv.org/abs/2411.09968
The ubiquity and value of tables as semi-structured data across various domains necessitate advanced methods for understanding their complexity and vast amounts of information. Despite the impressive capabilities of large language models (LLMs) in ad
Externí odkaz:
http://arxiv.org/abs/2411.08516
Autor:
Xiao, Yuxin, Wan, Chaoqun, Zhang, Yonggang, Wang, Wenxiao, Lin, Binbin, He, Xiaofei, Shen, Xu, Ye, Jieping
As the development and application of Large Language Models (LLMs) continue to advance rapidly, enhancing their trustworthiness and aligning them with human preferences has become a critical area of research. Traditional methods rely heavily on exten
Externí odkaz:
http://arxiv.org/abs/2411.02461
Autor:
Zhu, Yiheng, Wu, Jialu, Li, Qiuyi, Yan, Jiahuan, Yin, Mingze, Wu, Wei, Li, Mingyang, Ye, Jieping, Wang, Zheng, Wu, Jian
Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the development of machine learning algorithms for this task has seen signific
Externí odkaz:
http://arxiv.org/abs/2411.02120
Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and edi
Externí odkaz:
http://arxiv.org/abs/2410.23875
Autor:
Wang, Wenxiao, Gu, Lihui, Zhang, Liye, Luo, Yunxiang, Dai, Yi, Shen, Chen, Xie, Liang, Lin, Binbin, He, Xiaofei, Ye, Jieping
The exponential growth of knowledge and the increasing complexity of interdisciplinary research pose significant challenges for researchers, including information overload and difficulties in exploring novel ideas. The advancements in large language
Externí odkaz:
http://arxiv.org/abs/2410.23166