Zobrazeno 1 - 10
of 1 479
pro vyhledávání: '"Yu, En"'
Multi-modal Large Language Models (MLLMs) frequently face challenges from concept drift when dealing with real-world streaming data, wherein distributions change unpredictably. This mainly includes gradual drift due to long-tailed data and sudden dri
Externí odkaz:
http://arxiv.org/abs/2405.13459
Multiple Object Tracking (MOT) is a critical area within computer vision, with a broad spectrum of practical implementations. Current research has primarily focused on the development of tracking algorithms and enhancement of post-processing techniqu
Externí odkaz:
http://arxiv.org/abs/2403.04700
Autor:
Wei, Haoran, Kong, Lingyu, Chen, Jinyue, Zhao, Liang, Ge, Zheng, Yu, En, Sun, Jianjian, Han, Chunrui, Zhang, Xiangyu
Playing Large Vision Language Models (LVLMs) in 2023 is trendy among the AI community. However, the relatively large number of parameters (more than 7B) of popular LVLMs makes it difficult to train and deploy on consumer GPUs, discouraging many resea
Externí odkaz:
http://arxiv.org/abs/2401.12503
Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the
Externí odkaz:
http://arxiv.org/abs/2312.10841
Autor:
Yu, En, Zhao, Liang, Wei, Yana, Yang, Jinrong, Wu, Dongming, Kong, Lingyu, Wei, Haoran, Wang, Tiancai, Ge, Zheng, Zhang, Xiangyu, Tao, Wenbing
Humans possess the remarkable ability to foresee the future to a certain extent based on present observations, a skill we term as foresight minds. However, this capability remains largely under explored within existing Multimodal Large Language Model
Externí odkaz:
http://arxiv.org/abs/2312.00589
Atomic defects in solid-state materials are promising candidates for quantum interconnect and networking applications. Recently, a series of atomic defects have been identified in the silicon platform, where scalable device integration can be enabled
Externí odkaz:
http://arxiv.org/abs/2310.20014
Reducing noise interference is crucial for automatic speech recognition (ASR) in a real-world scenario. However, most single-channel speech enhancement (SE) generates "processing artifacts" that negatively affect ASR performance. Hence, in this study
Externí odkaz:
http://arxiv.org/abs/2308.12615
Autor:
Mila Nambiar, Yong Mong Bee, Yu En Chan, Ivan Ho Mien, Feri Guretno, David Carmody, Phong Ching Lee, Sing Yi Chia, Nur Nasyitah Mohamed Salim, Pavitra Krishnaswamy
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-12 (2024)
Abstract Pharmacotherapy guidelines for type 2 diabetes (T2D) emphasize patient-centered care, but applying this approach effectively in outpatient practice remains challenging. Data-driven treatment optimization approaches could enhance individualiz
Externí odkaz:
https://doaj.org/article/462293f0deb84f648ad8b773a74761af
Autor:
Zhao, Liang, Yu, En, Ge, Zheng, Yang, Jinrong, Wei, Haoran, Zhou, Hongyu, Sun, Jianjian, Peng, Yuang, Dong, Runpei, Han, Chunrui, Zhang, Xiangyu
Human-AI interactivity is a critical aspect that reflects the usability of multimodal large language models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of
Externí odkaz:
http://arxiv.org/abs/2307.09474
Autor:
Li, Zhuoling, Han, Chunrui, Ge, Zheng, Yang, Jinrong, Yu, En, Wang, Haoqian, Zhao, Hengshuang, Zhang, Xiangyu
Efficiency is quite important for 3D lane detection due to practical deployment demand. In this work, we propose a simple, fast, and end-to-end detector that still maintains high detection precision. Specifically, we devise a set of fully convolution
Externí odkaz:
http://arxiv.org/abs/2307.09472