🔥 Excited to announce the ICLR 2025 Workshop on AI for Children (AI4CHL)! pediamedai.com/ai4chl/ Join us as we bring together pediatricians, psychologists, educationists, and AI researchers to shape the future of AI for children's development, health, and education. 🌟 Why this workshop matters: Empowering children’s development with AI-driven education & mental health tools. Advancing pediatric healthcare through early diagnosis & interventions. Bridging gaps in low-resource regions with AI solutions. 💡 Let’s discuss opportunities, challenges, and the risks of AI for children, with a focus on emerging technologies like LLMs and Generative AI. 🗓️ Important Dates: Submission Deadline: Feb 11, 2025 Notification: March 04, 2025 ❗Submit your papers via OpenReview: openreview.net/group?id=ICLR… Let’s build a brighter future for children in the age of AI! 🌍✨ #AI4CHL #ICLR2025 #AIForGood #LLM

Come to #CVPR 2024 Poster #220 this morning to chat with our team about how to build the Vision-Language Model of the HD map and traffic scene understanding task. The updated version of our benchmark will serve as a new challenge dataset in the ITSC 2024 workshop. @RehgJim @yunshengmax @wy2029 @CanCui027 @ziran_w

🎉Excited to share one of our #CVPR2024 papers on Multimodal LLM for building the next generation autonomous driving system and HD map! Github Repo: github.com/LLVM-AD/MAPLM Previous Survey Repo: github.com/IrohXu/Awesome-Mu…

A Survey on Multimodal Large Language Models for Autonomous Driving Code page: github.com/IrohXu/Awesome-Mu… Paper page: arxiv.org/abs/2311.12320 With the emergence of Large Language Models (LLMs) and Vision Foundation Models (VFMs), multimodal AI systems benefiting from large models have the potential to equally perceive the real world, make decisions, and control tools as humans. In recent months, LLMs have shown widespread attention in autonomous driving and map systems. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors to apply in LLM driving systems. In this paper, we present a systematic investigation in this field. We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving. Then, we overview existing MLLM tools for driving, transportation, and map systems together with existing datasets and benchmarks. Moreover, we summarized the works in The 1st WACV Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD), which is the first workshop of its kind regarding LLMs in autonomous driving. To further promote the development of this field, we also discuss several important problems regarding using MLLMs in autonomous driving systems that need to be solved by both academia and industry. Happy to share our 1st WACV Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD) survey paper to everyone in the LLM & Autonomous driving communities. @_akhaliq @wacv_official @yunshengmax @wy2029 @pediamedai

LLVM-AD Workshop @wacv_official @TencentGlobal @_akhaliq The WACV Workshop on Large Language and Vision Models for Autonomous Driving seeks to bring academia and industry professionals in a collaborative exploration of applying large language and vision models to self-driving.