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20232025
most citedMTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering

5 citations · 10 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM

Han Wang, Yuxiang Nie, Yongjie Ye +6

The application of Large Vision-Language Models (LVLMs) for analyzing images and videos is an exciting and rapidly evolving field. In recent years, we've seen significant growth in…

cs.CL2024

A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding

Jinghui Lu, Haiyang Yu, Yanjie Wang +9

Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document…

cs.CV2024

What Makes Good Few-shot Examples for Vision-Language Models?

Zhaojun Guo, Jinghui Lu, Xuejing Liu +3

Despite the notable advancements achieved by leveraging pre-trained vision-language (VL) models through few-shot tuning for downstream tasks, our detailed empirical study highlight…

cs.CV2024★ 5 cited

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering

Jingqun Tang, Qi Liu, Yongjie Ye +14

Text-Centric Visual Question Answering (TEC-VQA) in its proper format not only facilitates human-machine interaction in text-centric visual environments but also serves as a de fac…

cs.CL2024★ 5 cited

PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition

Jinghui Lu, Ziwei Yang, Yanjie Wang +3

In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs). The main cause of high latency in LLMs is the sequential de…