activity
20212026
most citedQwen2.5-VL Technical Report

82 citations · 106 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV2026

DepthEvidence: Unifying Metric Depth Prediction and Geometric Reasoning in Multimodal Language Models

Jiangning Wei, Yuan Yao, Miaomiao Cui +4

Spatial reasoning with metric constraints requires linking objects to geometric measurements and preserving their numerical content during language reasoning. We present DepthEvide…

cs.CV2026

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…

cs.CV2026

Qwen3-VL-Seg: Unlocking Open-World Referring Segmentation with Vision-Language Grounding

Yuan Yao, Qiushi Yang, Humen Zhong +5

Open-world referring segmentation requires grounding unconstrained language expressions to precise pixel-level regions. Existing multimodal large language models (MLLMs) exhibit st…

cs.CV2025★ 16 cited

Qwen3-VL Technical Report

Shuai Bai, Yuxuan Cai, Ruizhe Chen +61

We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively…

cs.CV2025★ 82 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2023★ 2 cited

Modeling Entities as Semantic Points for Visual Information Extraction in the Wild

Zhibo Yang, Rujiao Long, Pengfei Wang +5

Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both the academia and industry, due to the wide range of real-world applications. Previous…

cs.CV2021★ 2 cited

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…

cs.CV2021★ 4 cited

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…