Zobrazeno 1 - 10
of 817
pro vyhledávání: '"Liu Jiaxiang"'
Publikováno v:
Kongzhi Yu Xinxi Jishu, Iss 4, Pp 96-104 (2023)
To address the issues in reliability bench tests for drive motor system of electric vehicle, particularly low correlation between reliability test conditions and real-world user driving scenarios, as well as non-equivalence in damage effects, a metho
Externí odkaz:
https://doaj.org/article/2175acf40e0e43abb960f34217813ac1
Publikováno v:
EMNLP 2024
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the reasoning paths and interpretability, which are crucial in clinical setti
Externí odkaz:
http://arxiv.org/abs/2412.13736
Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks. Compared to Cartesian methods, polar grids provide a viable alternative, offering better detail preservation in nearby spaces while covering larger areas. Ho
Externí odkaz:
http://arxiv.org/abs/2412.07616
Publikováno v:
NeurIPS 2024
Knowledge editing technology has received widespread attention for low-cost updates of incorrect or outdated knowledge in large-scale language models. However, recent research has found that edited models often exhibit varying degrees of performance
Externí odkaz:
http://arxiv.org/abs/2410.23843
Artificial intelligence has made significant strides in medical visual question answering (Med-VQA), yet prevalent studies often interpret images holistically, overlooking the visual regions of interest that may contain crucial information, potential
Externí odkaz:
http://arxiv.org/abs/2410.20327
Publikováno v:
ACCV2024
Multi-task-learning(MTL) is a multi-target optimization task. Neural networks try to realize each target using a shared interpretative space within MTL. However, as the scale of datasets expands and the complexity of tasks increases, knowledge sharin
Externí odkaz:
http://arxiv.org/abs/2410.03778
Medical Visual Question Answering (MedVQA), which offers language responses to image-based medical inquiries, represents a challenging task and significant advancement in healthcare. It assists medical experts to swiftly interpret medical images, the
Externí odkaz:
http://arxiv.org/abs/2404.12372
Mitigating the hallucinations of Large Language Models (LLMs) and enhancing them is a crucial task. Although some existing methods employ model self-enhancement techniques, they fall short of effectively addressing unknown factual hallucinations. Usi
Externí odkaz:
http://arxiv.org/abs/2402.09911
Geometric fracture assembly presents a challenging practical task in archaeology and 3D computer vision. Previous methods have focused solely on assembling fragments based on semantic information, which has limited the quantity of objects that can be
Externí odkaz:
http://arxiv.org/abs/2312.12340
Modern dataset search platforms employ ML task-based utility metrics instead of relying on metadata-based keywords to comb through extensive dataset repositories. In this setup, requesters provide an initial dataset, and the platform identifies compl
Externí odkaz:
http://arxiv.org/abs/2308.05637