10 citations · 12 across the 9 of their papers we have counts for
10 papers
Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
Gengluo Li, Pengyuan Lyu, Chengquan Zhang +7
Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditional cascaded pipelines depend…
Towards Training-Free Scene Text Editing
Yubo Li, Xugong Qin, Peng Zhang +3
Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific trainin…
MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation
Gengluo Li, Chengquan Zhang, Yupu Liang +9
End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding.…
Gather and Trace: Rethinking Video TextVQA from an Instance-oriented Perspective
Yan Zhang, Gangyan Zeng, Daiqing Wu +5
Video text-based visual question answering (Video TextVQA) aims to answer questions by explicitly reading and reasoning about the text involved in a video. Most works in this field…
When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding
Yan Shu, Hangui Lin, Yexin Liu +7
Large Multimodal Models (LMMs) have achieved impressive progress in visual perception and reasoning. However, when confronted with visually ambiguous or non-semantic scene text, th…
VidText: Towards Comprehensive Evaluation for Video Text Understanding
Zhoufaran Yang, Yan Shu, Jing Wang +8
Visual texts embedded in videos carry rich semantic information, which is crucial for both holistic video understanding and fine-grained reasoning about local human actions. Howeve…
Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues
Yan Zhang, Gangyan Zeng, Huawen Shen +3
Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly reasoning textual and visual information in a given video. I…
Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval
Gangyan Zeng, Yuan Zhang, Jin Wei +5
Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which r…
TextBlockV2: Towards Precise-Detection-Free Scene Text Spotting with Pre-trained Language Model
Jiahao Lyu, Jin Wei, Gangyan Zeng +4
Existing scene text spotters are designed to locate and transcribe texts from images. However, it is challenging for a spotter to achieve precise detection and recognition of scene…
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Chengyang Fang, Gangyan Zeng, Yu Zhou +4
Texts in scene images convey critical information for scene understanding and reasoning. The abilities of reading and reasoning matter for the model in the text-based visual questi…