82 citations · 91 across the 11 of their papers we have counts for
12 papers
Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving
Xin Zhou, Zongchuang Zhao, Zhibo Yang +13
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-langu…
Contrastive On-Policy Distillation
Jiacheng Ruan, Jun Tang, Wenzhen Yuan +5
On-policy distillation (OPD) trains a student model on trajectories sampled from its own policy, providing dense token-level supervision by minimizing the divergence between the te…
Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis
Zhipeng Xu, Zulong Chen, Qing Liu +6
Key Information Extraction (KIE) converts visually rich documents into structured data, but practical deployment remains challenging: strong performance often relies on costly on-s…
CC-OCR V2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding
Chunyi Peng, Zhipeng Xu, Yuqi Xiong +12
Recent Large Multimodal Models (LMMs) have achieved remarkable progress on OCR-centric document understanding and processing tasks. Existing benchmarks primarily evaluate LMMs acro…
Qwen2.5-VL Technical Report
Shuai Bai, Keqin Chen, Xuejing Liu +24
We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative func…
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
Wenwen Yu, Zhibo Yang, Jianqiang Wan +5
Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding and the emergence of large language…
CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy
Zhibo Yang, Jun Tang, Zhaohai Li +9
Large Multimodal Models (LMMs) have demonstrated impressive performance in recognizing document images with natural language instructions. However, it remains unclear to what exten…
VL-Reader: Vision and Language Reconstructor is an Effective Scene Text Recognizer
Humen Zhong, Zhibo Yang, Zhaohai Li +4
Text recognition is an inherent integration of vision and language, encompassing the visual texture in stroke patterns and the semantic context among the character sequences. Towar…
Platypus: A Generalized Specialist Model for Reading Text in Various Forms
Peng Wang, Zhaohai Li, Jun Tang +4
Reading text from images (either natural scenes or documents) has been a long-standing research topic for decades, due to the high technical challenge and wide application range. P…
Vision-Language Pre-Training for Boosting Scene Text Detectors
Sibo Song, Jianqiang Wan, Zhibo Yang +4
Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning…
ARTS: Eliminating Inconsistency between Text Detection and Recognition with Auto-Rectification Text Spotter
Humen Zhong, Jun Tang, Wenhai Wang +3
Recent approaches for end-to-end text spotting have achieved promising results. However, most of the current spotters were plagued by the inconsistency problem between text detecti…
MOST: A Multi-Oriented Scene Text Detector with Localization Refinement
Minghang He, Minghui Liao, Zhibo Yang +6
Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they m…