Zobrazeno 1 - 10
of 922
pro vyhledávání: '"Zhang, Xiaokang"'
Monitoring changes triggered by mining activities is crucial for industrial controlling, environmental management and regulatory compliance, yet it poses significant challenges due to the vast and often remote locations of mining sites. Remote sensin
Externí odkaz:
http://arxiv.org/abs/2407.03971
Autor:
Ma, Zeyao, Zhang, Bohan, Zhang, Jing, Yu, Jifan, Zhang, Xiaokang, Zhang, Xiaohan, Luo, Sijia, Wang, Xi, Tang, Jie
We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LLMs) in the actual workflow of spreadsheet users. Unlike existing bench
Externí odkaz:
http://arxiv.org/abs/2406.14991
Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications. Nowadays, accurate semantic segmentation of remote sensing images remains a challenge due
Externí odkaz:
http://arxiv.org/abs/2406.10828
Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of
Externí odkaz:
http://arxiv.org/abs/2405.04858
Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature. It is typically regarded as a pixel-wise labeling task that aims to classify each pixel as changed or unchanged. Although per-pi
Externí odkaz:
http://arxiv.org/abs/2404.12081
Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations. Current factuality probes, trained using humanannotated labels, exhibit limited transferability to out-of-distribution c
Externí odkaz:
http://arxiv.org/abs/2404.06742
Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) techniques has significantly advanced deep-learning applications in the geosciences. Recently, with its ingenious and versatile architectu
Externí odkaz:
http://arxiv.org/abs/2404.04531
Semantic segmentation of remote sensing images is a fundamental task in geoscience research. However, there are some significant shortcomings for the widely used convolutional neural networks (CNNs) and Transformers. The former is limited by its insu
Externí odkaz:
http://arxiv.org/abs/2404.02457
Autor:
Zhang, Xiaokang, Zhang, Jing, Ma, Zeyao, Li, Yang, Zhang, Bohan, Li, Guanlin, Yao, Zijun, Xu, Kangli, Zhou, Jinchang, Zhang-Li, Daniel, Yu, Jifan, Zhao, Shu, Li, Juanzi, Tang, Jie
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office
Externí odkaz:
http://arxiv.org/abs/2403.19318
Remote sensing image super-resolution (SR) is a crucial task to restore high-resolution (HR) images from low-resolution (LR) observations. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has shown promising performance in image reconstru
Externí odkaz:
http://arxiv.org/abs/2403.11078