Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Gong, YongShun"'
Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task. In this paper, we experimentally analyze that existing transfo
Externí odkaz:
http://arxiv.org/abs/2412.13232
Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding tasks, as the complex geometric details in scenes increase the difficulty of fitting the grad
Externí odkaz:
http://arxiv.org/abs/2411.16308
Deep learning underpins most of the currently advanced natural language processing (NLP) tasks such as textual classification, neural machine translation (NMT), abstractive summarization and question-answering (QA). However, the robustness of the mod
Externí odkaz:
http://arxiv.org/abs/2411.08248
Autor:
Yuan, Shilu, Li, Dongfeng, Liu, Wei, Zhang, Xinxin, Chen, Meng, Zhang, Junjie, Gong, Yongshun
Fine-grained urban flow inference (FUFI) is a crucial transportation service aimed at improving traffic efficiency and safety. FUFI can infer fine-grained urban traffic flows based solely on observed coarse-grained data. However, most of existing met
Externí odkaz:
http://arxiv.org/abs/2406.09710
Autor:
Wang, Qikai, He, Rundong, Gong, Yongshun, Ren, Chunxiao, Sun, Haoliang, Huang, Xiaoshui, Yin, Yilong
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce. However, real-world unlabeled data often contain unseen-class samples, which can hinder the classification of s
Externí odkaz:
http://arxiv.org/abs/2405.16093
Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges. The lack of a comprehensive assessment hampers determining whether these models truly represent advan
Externí odkaz:
http://arxiv.org/abs/2404.14678
Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-distribution (ID) images. Up to now, a mainstream strategy is based on lar
Externí odkaz:
http://arxiv.org/abs/2404.00323
Autor:
Zan, Daoguang, Yu, Ailun, Liu, Wei, Chen, Dong, Shen, Bo, Li, Wei, Yao, Yafen, Gong, Yongshun, Chen, Xiaolin, Guan, Bei, Yang, Zhiguang, Wang, Yongji, Wang, Qianxiang, Cui, Lizhen
The impressive performance of large language models (LLMs) on code-related tasks has shown the potential of fully automated software development. In light of this, we introduce a new software engineering task, namely Natural Language to code Reposito
Externí odkaz:
http://arxiv.org/abs/2403.16443
Autor:
Liu, Dingning, Huang, Xiaoshui, Hou, Yuenan, Wang, Zhihui, Yin, Zhenfei, Gong, Yongshun, Gao, Peng, Ouyang, Wanli
In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point cloud scenes. This framework empowers users to effortlessly generate and
Externí odkaz:
http://arxiv.org/abs/2402.03327
Autor:
Liu, Dingning, Dong, Xiaomeng, Zhang, Renrui, Luo, Xu, Gao, Peng, Huang, Xiaoshui, Gong, Yongshun, Wang, Zhihui
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks. Our investigation reveals that while GPT-4V exhibits proficiency in discerning the position and
Externí odkaz:
http://arxiv.org/abs/2312.09738