82 citations · 102 across the 20 of their papers we have counts for
15 papers · 1 filter
Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification
Wujian Peng, Lingchen Meng, Yuxuan Cai +7
Unified Multimodal Modeling aims to integrate visual understanding and generation within a single system. However, existing approaches typically rely on two disparate visual tokeni…
GenMask: Adapting DiT for Segmentation via Direct Mask Generation
Yuhuan Yang, Xianwei Zhuang, Yuxuan Cai +6
Recent approaches for segmentation have leveraged pretrained generative models as feature extractors, treating segmentation as a downstream adaptation task via indirect feature ret…
HopChain: Multi-Hop Data Synthesis for Generalizable Vision-Language Reasoning
Shenzhi Wang, Shixuan Liu, Jing Zhou +8
Vision-language models (VLMs) show strong multimodal capabilities but still struggle with fine-grained vision-language reasoning. We find that long chain-of-thought (CoT) reasoning…
Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos
Songtao Jiang, Sibo Song, Chenyi Zhou +12
The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion…
From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning
Ruilin Luo, Chufan Shi, Yizhen Zhang +10
The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this…
CodePercept: Code-Grounded Visual STEM Perception for MLLMs
Tongkun Guan, Zhibo Yang, Jianqiang Wan +10
When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual deficiencies or reasoning limit…
UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents
Yifan Ji, Zhipeng Xu, Zhenghao Liu +7
Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual quality, and task-specific information req…
VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
Jianke Zhang, Xiaoyu Chen, Qiuyue Wang +7
Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising…
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…
Revisiting Multimodal Positional Encoding in Vision-Language Models
Jie Huang, Xuejing Liu, Sibo Song +4
Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensi…
Qwen-Image Technical Report
Chenfei Wu, Jiahao Li, Jingren Zhou +36
We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address th…
Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think
Liang Chen, Shuai Bai, Wenhao Chai +5
The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transfor…
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…
SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild
Jiawei Liu, Yuanzhi Zhu, Feiyu Gao +5
Generating visual text in natural scene images is a challenging task with many unsolved problems. Different from generating text on artificially designed images (such as posters, c…
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…