Large vision-language models (LVLMs) employ multi-modal in-context learning (MM-ICL) to adapt to new tasks by leveraging demonstration examples. While increasing the number of demonstrations boosts performance, they incur significant inference latency due to the quadratic computational cost of Transformer attention with respect to the context length. To address this trade-off, we propose Parallel In-Context Learning (Parallel-ICL), a plug-and-play inference algorithm. Parallel-ICL partitions the long demonstration context into multiple shorter, manageable chunks. It processes these chunks in parallel and integrates their predictions at the logit level, using a weighted Product-of-Experts (PoE) ensemble to approximate the full-context output. Guided by ensemble learning theory, we introduce principled strategies for Parallel-ICL: (i) clustering-based context chunking to maximize inter-chunk diversity and (ii) similarity-based context compilation to weight predictions by query relevance. Extensive experiments on VQA, image captioning, and classification benchmarks demonstrate that Parallel-ICL achieves performance comparable to full-context MM-ICL, while significantly improving inference speed. Our work offers an effective solution to the accuracy-efficiency trade-off in MM-ICL, enabling dynamic task adaptation with substantially reduced inference overhead.
LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding
The Hong Kong Polytechnic University（香港理工大学）；Princeton University（普林斯顿大学）；Nanjing University（南京大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Prompt Tuning with Soft Context Sharing for Vision-Language Models
State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Engineering Laboratory for Intelligent Industrial Vision, CASIA（中国科学院自动化研究所智能工业视觉工程实验室）；Research Center of Aerospace Information, CASIA（中国科学院自动化研究所航天信息研究中心）
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；CPII under InnoHK（创新香港科技促进会）；MThreads AI
VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
1 Institute of Automation, Chinese Academy of Sciences；2 School of Artificial Intelligence, University of Chinese Academy of Sciences；3 Centre for Artificial Intelligence；Robotics, Hong Kong Institute of Science \& Innovation, CAS；4 Independent Researcher
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机学院）；Cognitive AI Lab, Shanghai Huawei Technologies, China（上海华为技术有限公司认知人工智能实验室）
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Peking University（北京大学）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；BIGAI（北京智源人工智能研究院）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Beijing Jiaotong University（北京交通大学）
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
CSE Department, HKUST（香港科技大学计算机科学与工程系）；Tencent AI Seattle Lab（腾讯AI西雅图实验室）；University of Edinburgh（爱丁堡大学）；NVIDIA AI Technology Center (NVAITC), NVIDIA, Santa Clara, USA（英伟达圣克拉拉人工智能技术中心）
Contextualized Visual Personalization in Vision-Language Models
Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea（电气电子工程系，首尔国立大学，首尔，韩国）；Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Korea（人工智能交叉学科项目，首尔国立大学，首尔，韩国）
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Sydney AI Centre, The University of Sydney（悉尼大学悉尼人工智能中心）；Institute of Image Communication and Network Engineering, Shanghai JiaoTong University（上海交通大学图像通信与网络工程研究所）
