Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

• URL: http://arxiv.org/abs/2402.02968v2
• Date: Sun, 26 May 2024 11:54:10 GMT
• Title: Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
• Authors: Sheng Luo, Wei Chen, Wanxin Tian, Rui Liu, Luanxuan Hou, Xiubao Zhang, Haifeng Shen, Ruiqi Wu, Shuyi Geng, Yi Zhou, Ling Shao, Yi Yang, Bojun Gao, Qun Li, Guobin Wu,
• Abstract summary: We present a systematic analysis of MM-VUFMs specifically designed for road scenes. Our objective is to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques. We provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models.
• Score: 56.2139730920855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems. In the context of intelligent vehicles, leveraging the power of foundation models has proven to be transformative, offering notable advancements in visual understanding. Equipped with multi-modal and multi-task learning capabilities, multi-modal multi-task visual understanding foundation models (MM-VUFMs) effectively process and fuse data from diverse modalities and simultaneously handle various driving-related tasks with powerful adaptability, contributing to a more holistic understanding of the surrounding scene. In this survey, we present a systematic analysis of MM-VUFMs specifically designed for road scenes. Our objective is not only to provide a comprehensive overview of common practices, referring to task-specific models, unified multi-modal models, unified multi-task models, and foundation model prompting techniques, but also to highlight their advanced capabilities in diverse learning paradigms. These paradigms include open-world understanding, efficient transfer for road scenes, continual learning, interactive and generative capability. Moreover, we provide insights into key challenges and future trends, such as closed-loop driving systems, interpretability, embodied driving agents, and world models. To facilitate researchers in staying abreast of the latest developments in MM-VUFMs for road scenes, we have established a continuously updated repository at https://github.com/rolsheng/MM-VUFM4DS

Related papers

• HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
  Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities. It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
  arXiv  Detail & Related papers  (2024-07-03T18:00:48Z)
• From Efficient Multimodal Models to World Models: A Survey [28.780451336834876]
  Multimodal Large Models (MLMs) are becoming a significant research focus combining powerful language models with multimodal learning. This review explores the latest developments and challenges in large instructions, emphasizing their potential in achieving artificial general intelligence.
  arXiv  Detail & Related papers  (2024-06-27T15:36:43Z)
• Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions [11.786387517781328]
  Vision-Language Models (VLMs) are advanced models that can tackle more intricate tasks such as image captioning and visual question answering. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs. We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible.
  arXiv  Detail & Related papers  (2024-02-20T18:57:34Z)
• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• Generative Multimodal Models are In-Context Learners [60.50927925426832]
  We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning.
  arXiv  Detail & Related papers  (2023-12-20T18:59:58Z)
• Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning [49.92517970237088]
  We tackle the problem of training a robot to understand multimodal prompts. This type of task poses a major challenge to robots' capability to understand the interconnection and complementarity between vision and language signals. We introduce an effective framework that learns a policy to perform robot manipulation with multimodal prompts.
  arXiv  Detail & Related papers  (2023-10-14T22:24:58Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)
• Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications [47.501121601856795]
  Multimodality Representation Learning is a technique of learning to embed information from different modalities and their correlations. Cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task. This survey presents the literature on the evolution and enhancement of deep learning multimodal architectures.
  arXiv  Detail & Related papers  (2023-02-01T11:48:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.