Recent large vision-language models (LVLMs) have been applied to diverse VQA tasks. However, achieving practical performance typically requires task-specific fine-tuning with large numbers of image-text pairs, which are costly to collect. In this work, we study text-centric training, a setting where only textual descriptions are available and no real images are provided, as a paradigm for low-cost data scaling. Unlike images, whose collection is often restricted by privacy constraints and scarcity in niche domains, text is widely available. Moreover, text is easily editable, enabling automatic diversification and expansion with LLMs at minimal human effort. While this offers clear advantages over image collection in terms of scalability and cost, training on raw text without images still yields limited gains on VQA tasks because of the image-text modality gap. To address this issue, we propose a Text-Printed Image (TPI), which generates synthetic images by directly rendering the given textual description on a plain white canvas. This simple rendering projects text into the image modality and can be integrated into arbitrary existing LVLM training pipelines at low cost. Moreover, TPI preserves the semantics of the text, whereas text-to-image models often fail to do. Across four models and seven benchmarks, our systematic experiments show that TPI enables more effective text-centric training than synthetic images generated by a diffusion model. We further explore TPI as a low-cost data-augmentation strategy and demonstrate its practical utility. Overall, our findings highlight the significant potential of text-centric training and, more broadly, chart a path toward fully automated data generation for LVLMs.
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
PANDA: An LLM-Enhanced Performance-Driven Analog Design Framework Bridging Design Intent and Layout Generation
School of Integrated Circuits, Peking University（集成电路学院，北京大学）；Beijing Advanced Innovation Center for Integrated Circuits（北京集成电路先进创新中心）；Institute of Electronic Design Automation, Peking University（电子设计自动化研究所，北京大学）
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
Om AI Research（Om AI研究机构）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（东华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Institute of High Performance Computing, A*STAR（高性能计算研究所，A*STAR）
MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition
Institute of Computer Science, ZHAW（苏黎世应用科技大学计算机科学学院）；People and Computing Laboratory, University of Zurich（苏黎世大学人与计算实验室）；Centre for Artificial Intelligence, ZHAW（苏黎世应用科技大学人工智能中心）；European Centre for Living Technology (ECLT)（欧洲生命技术中心）
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Fraunhofer Institute for Open Communication Systems（弗劳恩霍夫开放通信系统研究所）；Technical University of Munich（慕尼黑技术大学）；Alibaba International Digital Commerce（阿里巴巴国际数字商务）
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)（博世北美研究部及博世人工智能中心（BCAI））
Max-Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Amazon Web Services（亚马逊云科技）；ETH Zürich（苏黎世联邦理工学院）
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric re…
VepAgent: Bridging Causal-Transition via Tool-Augmented Reinforcement Learning for Video Event Prediction
Nankai University（南开大学）；Carnegie Mellon University（卡内基梅隆大学）；Xiaohongshu Inc.（小红书公司）；Columbia University（哥伦比亚大学）；Santa Clara University（圣克拉拉大学）；New York University（纽约大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Bridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（深圳市大数据研究院，香港中文大学（深圳））；Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau（澳门大学智慧城市物联网国家重点实验室）
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院眼科学系）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
OpenGVLab, Shanghai AI Laboratory（上海人工智能实验室OpenGVLab）；The University of Tokyo（东京大学）；Nanjing University（南京大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；USTC（中国科学技术大学）；SIAT, CAS（中国科学院深圳先进技术研究院）
Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）
Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at inference. Two popular forms of weak-supervision used in open-vocabulary detection (OVD) include pretrained CLIP model and image-level supervision. We note that both these modes of supervision are not optimally aligned for the detection task: CLIP is trained with image-text pairs and lacks precise localization of objects while the image-level supervision has been used with heuristics that do not accurately specify local …
Bridging Human Evaluation to Infrared and Visible Image Fusion
School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学与技术学院）
GeoBridge++: Fact-Guided Geo-Semantic Bridging for Unified Cross-View Geo-Localization
School of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）
CI-ICM: Channel Importance-driven Learned Image Coding for Machines
School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-Sen University（中山大学深圳校区电子与通信工程学院）；School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Faculty of Electrical Engineering and Computer Science, Ningbo University（宁波大学电气工程与计算机科学学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
