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pro vyhledávání: '"Wang, ZiXiao"'
Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. The limitation lies in the insufficient exploration of character morphologies, including the monotonousn
Externí odkaz:
http://arxiv.org/abs/2411.15585
Existing scene text removal (STR) task suffers from insufficient training data due to the expensive pixel-level labeling. In this paper, we aim to address this issue by introducing a Text-aware Masked Image Modeling algorithm (TMIM), which can pretra
Externí odkaz:
http://arxiv.org/abs/2409.13431
Autor:
Wang, Zixiao, Fan, Jicong
Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few methods using graph kernels or graph neural networks (GNNs). Graph kern
Externí odkaz:
http://arxiv.org/abs/2408.11370
Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging texts that involve factors such as severely distorted and perspective char
Externí odkaz:
http://arxiv.org/abs/2407.05562
Few-shot gradient methods have been extensively utilized in existing model pruning methods, where the model weights are regarded as static values and the effects of potential weight perturbations are not considered. However, the widely used large lan
Externí odkaz:
http://arxiv.org/abs/2406.07017
In text recognition, self-supervised pre-training emerges as a good solution to reduce dependence on expansive annotated real data. Previous studies primarily focus on local visual representation by leveraging mask image modeling or sequence contrast
Externí odkaz:
http://arxiv.org/abs/2405.05841
Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention. In this paper, we propose ChatPattern, a novel Large-Language-Model (LLM) powered framework for flexible p
Externí odkaz:
http://arxiv.org/abs/2403.15434
Emerging industrial applications involving robotic collaborative operations and mobile robots require a more reliable and precise wireless network for deterministic data transmission. To meet this demand, the 3rd Generation Partnership Project (3GPP)
Externí odkaz:
http://arxiv.org/abs/2401.17721
The discrete distribution is often used to describe complex instances in machine learning, such as images, sequences, and documents. Traditionally, clustering of discrete distributions (D2C) has been approached using Wasserstein barycenter methods. T
Externí odkaz:
http://arxiv.org/abs/2401.13913
Autor:
Zhang, Yu, Wang, Zixiao, Zhao, Jin, Guo, Yuluo, Yu, Hui, Huang, Zhiying, Shi, Xuanhua, Liao, Xiaofei
Modern scientific applications predominantly run on large-scale computing platforms, necessitating collaboration between scientific domain experts and high-performance computing (HPC) experts. While domain experts are often skilled in customizing dom
Externí odkaz:
http://arxiv.org/abs/2312.04900