125 citations · 133 across the 13 of their papers we have counts for
13 papers
Unlimited OCR Works
Youyang Yin, Huanhuan Liu, YY +14
Recently, end-to-end OCR models, exemplified by DeepSeek OCR, have once again thrust OCR into the spotlight. A widely held view is that employing a large language model (LLM) as th…
OCR-Reasoning Benchmark: Unveiling the True Capabilities of MLLMs in Complex Text-Rich Image Reasoning
Mingxin Huang, Yongxin Shi, Dezhi Peng +3
Recent advancements in multimodal slow-thinking systems have demonstrated remarkable performance across various visual reasoning tasks. However, their capabilities in text-rich ima…
Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid
Mingxin Huang, Yuliang Liu, Dingkang Liang +2
Recently, scaling images to high resolution has received much attention in multimodal large language models (MLLMs). Most existing practices adopt a sliding-window-style cropping s…
Bridging the Gap Between End-to-End and Two-Step Text Spotting
Mingxin Huang, Hongliang Li, Yuliang Liu +2
Modularity plays a crucial role in the development and maintenance of complex systems. While end-to-end text spotting efficiently mitigates the issues of error accumulation and sub…
VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization
Yuliang Liu, Mingxin Huang, Hao Yan +6
Text spotting, a task involving the extraction of textual information from image or video sequences, faces challenges in cross-domain adaption, such as image-to-image and image-to-…
SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting
Mingxin Huang, Dezhi Peng, Hongliang Li +6
End-to-end scene text spotting, which aims to read the text in natural images, has garnered significant attention in recent years. However, recent state-of-the-art methods usually…
Progressive Evolution from Single-Point to Polygon for Scene Text
Linger Deng, Mingxin Huang, Xudong Xie +3
The advancement of text shape representations towards compactness has enhanced text detection and spotting performance, but at a high annotation cost. Current models use single-poi…
Hierarchical Side-Tuning for Vision Transformers
Weifeng Lin, Ziheng Wu, Wentao Yang +3
Fine-tuning pre-trained Vision Transformers (ViTs) has showcased significant promise in enhancing visual recognition tasks. Yet, the demand for individualized and comprehensive fin…
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
Mingxin Huang, Jiaxin Zhang, Dezhi Peng +5
In recent years, end-to-end scene text spotting approaches are evolving to the Transformer-based framework. While previous studies have shown the crucial importance of the intrinsi…
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
Yuliang Liu, Zhang Li, Mingxin Huang +7
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks…
SPTS v2: Single-Point Scene Text Spotting
Yuliang Liu, Jiaxin Zhang, Dezhi Peng +8
End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotation…
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition
Mingxin Huang, Yuliang Liu, Zhenghao Peng +6
End-to-end scene text spotting has attracted great attention in recent years due to the success of excavating the intrinsic synergy of the scene text detection and recognition. How…
SPTS: Single-Point Text Spotting
Dezhi Peng, Xinyu Wang, Yuliang Liu +9
Existing scene text spotting (i.e., end-to-end text detection and recognition) methods rely on costly bounding box annotations (e.g., text-line, word-level, or character-level boun…