Zobrazeno 1 - 10
of 20 631
pro vyhledávání: '"Zhao, Liang"'
Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification. Traditional eXplainable AI (XAI) methods typically rely on unimodal e
Externí odkaz:
http://arxiv.org/abs/2411.13053
As an essential visual attribute, image complexity affects human image comprehension and directly influences the performance of computer vision tasks. However, accurately assessing and quantifying image complexity faces significant challenges. Previo
Externí odkaz:
http://arxiv.org/abs/2411.12792
Autor:
Zhao, Liang, Geng, Shenglin, Tang, Xiongyan, Hawbani, Ammar, Sun, Yunhe, Xu, Lexi, Tarchi, Daniele
Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data colle
Externí odkaz:
http://arxiv.org/abs/2411.07752
Autor:
Ma, Yiyang, Liu, Xingchao, Chen, Xiaokang, Liu, Wen, Wu, Chengyue, Wu, Zhiyu, Pan, Zizheng, Xie, Zhenda, Zhang, Haowei, yu, Xingkai, Zhao, Liang, Wang, Yisong, Liu, Jiaying, Ruan, Chong
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method
Externí odkaz:
http://arxiv.org/abs/2411.07975
Autor:
Li, Longyan, Ning, Chao, Pan, Guangsheng, Zhang, Leiqi, Gu, Wei, Zhao, Liang, Du, Wenli, Shahidehpour, Mohammad
This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel m
Externí odkaz:
http://arxiv.org/abs/2410.20485
With the rapid advancement of autonomous driving technology, efficient and accurate object detection capabilities have become crucial factors in ensuring the safety and reliability of autonomous driving systems. However, in low-visibility environment
Externí odkaz:
http://arxiv.org/abs/2410.17734
Representation learning of Text-Attributed Graphs (TAGs) has garnered significant attention due to its applications in various domains, including recommendation systems and social networks. Despite advancements in TAG learning methodologies, challeng
Externí odkaz:
http://arxiv.org/abs/2410.15268
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size, yet existing methods assume public access to calibration data, which i
Externí odkaz:
http://arxiv.org/abs/2410.14852
Autor:
Huang, Lei, Feng, Xiaocheng, Ma, Weitao, Zhao, Liang, Fan, Yuchun, Zhong, Weihong, Xu, Dongliang, Yang, Qing, Liu, Hongtao, Qin, Bing
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems. However, improving this capability requires high-quality attribution data,
Externí odkaz:
http://arxiv.org/abs/2410.13298
In computer-assisted orthopedic surgery (CAOS), accurate pre-operative to intra-operative bone registration is an essential and critical requirement for providing navigational guidance. This registration process is challenging since the intra-operati
Externí odkaz:
http://arxiv.org/abs/2410.03078