1 citations · 1 across the 6 of their papers we have counts for
6 papers
ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction
Bojun Zhang, Junhong Liang, Feifei Zhai +2
Vision Language Models (VLMs) have shown great success in general visual tasks, yet they still struggle to deeply understand text within images. In this paper, we introduce ReViCo…
ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services
Fengxian Ji, Jingpu Yang, Zirui Song +5
Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks. However, their performance on paid, real-wor…
Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation
Junhong Liang, Yifan Lu, Ekaterina Kochmar +1
Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require \emph{learner-friendly pedagogical feedback} that is act…
Vision Language Models Are Not (Yet) Spelling Correctors
Junhong Liang, Bojun Zhang
Spelling correction from visual input poses unique challenges for vision language models (VLMs), as it requires not only detecting but also correcting textual errors directly withi…
PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction
Junhong Liang, Bojun Zhang
Chinese ASR correction is challenging because errors are often \emph{phonetic} (many characters share similar Pinyin) while the correction model must also obey a \emph{length const…
An Analysis of Language Frequency and Error Correction for Esperanto
Junhong Liang
Current Grammar Error Correction (GEC) initiatives tend to focus on major languages, with less attention given to low-resource languages like Esperanto. In this article, we begin t…