Zobrazeno 1 - 10
of 3 112
pro vyhledávání: '"Chen, JiaQi"'
Noisy labels threaten the robustness of few-shot learning (FSL) due to the inexact features in a new domain. CLIP, a large-scale vision-language model, performs well in FSL on image-text embedding similarities, but it is susceptible to misclassificat
Externí odkaz:
http://arxiv.org/abs/2412.12793
Autor:
Chen, Jiaqi, Zhu, Xiaoye, Liu, Tianyang, Chen, Ying, Chen, Xinhui, Yuan, Yiwen, Leong, Chak Tou, Li, Zuchao, Long, Tang, Zhang, Lei, Yan, Chenyu, Mei, Guanghao, Zhang, Jie, Zhang, Lefei
Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Although past methods have achieved good performance on detecting pure machine-generated text, those detectors have po
Externí odkaz:
http://arxiv.org/abs/2412.10432
In this paper, we give the analytic expression for homogeneous part of solutions of arbitrary tree-level cosmological correlators, including massive propagators and time-derivative interactions cases. The solutions are given in the form of multivaria
Externí odkaz:
http://arxiv.org/abs/2411.03088
Human action recognition (HAR) plays a key role in various applications such as video analysis, surveillance, autonomous driving, robotics, and healthcare. Most HAR algorithms are developed from RGB images, which capture detailed visual information.
Externí odkaz:
http://arxiv.org/abs/2410.16746
Autor:
Zhang, Jiayi, Xiang, Jinyu, Yu, Zhaoyang, Teng, Fengwei, Chen, Xionghui, Chen, Jiaqi, Zhuge, Mingchen, Cheng, Xin, Hong, Sirui, Wang, Jinlin, Zheng, Bingnan, Liu, Bang, Luo, Yuyu, Wu, Chenglin
Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these wor
Externí odkaz:
http://arxiv.org/abs/2410.10762
Autor:
Chen, Jiaqi, Feng, Bo
We give an explanation of the $\mathrm{d}\log$-form of the coefficient matrix of canonical differential equations using the projection of ($n$+1)-$\mathrm{d}\log$ forms onto $n$-$\mathrm{d}\log$ forms. This projection is done using the leading-order
Externí odkaz:
http://arxiv.org/abs/2409.12663
Identifying the physical properties of the surrounding environment is essential for robotic locomotion and navigation to deal with non-geometric hazards, such as slippery and deformable terrains. It would be of great benefit for robots to anticipate
Externí odkaz:
http://arxiv.org/abs/2408.16567
LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) task. However, existing LLM-based methods often focus only on solving high-level task planning by selecting nodes in predefined navigation graphs
Externí odkaz:
http://arxiv.org/abs/2407.05890
Autor:
Chen, Jiaqi
Publikováno v:
PoS(LL2024)035
The matrix of canonical differential equations consists of the 1-$\mathrm{d}\log$-form coefficients obtained by projecting ($n$+1)-$\mathrm{d}\log$-forms onto $n$-$\mathrm{d}\log$-form master integrands. With dual form in relative cohomology, the int
Externí odkaz:
http://arxiv.org/abs/2407.03562
Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing photorealistic views from multi-view images of static scenes, but face challenges in dynamic, real-world environments with distractors like moving objects, shadows, and ligh
Externí odkaz:
http://arxiv.org/abs/2405.18715