
Analyzing the spectrum of relative likelihood scores of texts, using Fourier transform, can effectively distinguish between human-written and model-generated texts, revealing subtle differences in language use.


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detecting-subtle-differences-between-human-written-and-model-generated-texts-using-the-spectrum-of-relative-likelihood


Detecting Subtle Differences between Human-Written and Model-Generated Texts Using the Spectrum of Relative Likelihood


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提案されたContext-aware and Boundary-guided Network（CBN）は、セグメンテーションベースのシーンテキスト検出において、高速性と精度のトレードオフを実現し、最先端の結果を達成する。


cbnet-a-context-aware-and-boundary-guided-network-for-segmentation-based-scene-text-detection


CBNet: A Context-aware and Boundary-guided Network for Segmentation-Based Scene Text Detection



Proposing a Context-aware and Boundary-guided Network (CBN) to enhance text detection by improving segmentation results and expanding text kernels adaptively.


cbnet-a-context-aware-network-for-scene-text-detection


CBNet: A Context-Aware Network for Scene Text Detection
