Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions

• URL: http://arxiv.org/abs/2502.15336v1
• Date: Fri, 21 Feb 2025 09:41:27 GMT
• Title: Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions
• Authors: Shoubin Chen, Zehao Wu, Kai Zhang, Chunyu Li, Baiyang Zhang, Fei Ma, Fei Richard Yu, Qingquan Li,
• Abstract summary: Embodied multimodal large models (EMLMs) have gained significant attention in recent years due to their potential to bridge the gap between perception, cognition, and action in complex, real-world environments.<n>This comprehensive review explores the development of such models, including Large Language Models (LLMs), Large Vision Models (LVMs), and other models.
• Score: 16.78870612041548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embodied multimodal large models (EMLMs) have gained significant attention in recent years due to their potential to bridge the gap between perception, cognition, and action in complex, real-world environments. This comprehensive review explores the development of such models, including Large Language Models (LLMs), Large Vision Models (LVMs), and other models, while also examining other emerging architectures. We discuss the evolution of EMLMs, with a focus on embodied perception, navigation, interaction, and simulation. Furthermore, the review provides a detailed analysis of the datasets used for training and evaluating these models, highlighting the importance of diverse, high-quality data for effective learning. The paper also identifies key challenges faced by EMLMs, including issues of scalability, generalization, and real-time decision-making. Finally, we outline future directions, emphasizing the integration of multimodal sensing, reasoning, and action to advance the development of increasingly autonomous systems. By providing an in-depth analysis of state-of-the-art methods and identifying critical gaps, this paper aims to inspire future advancements in EMLMs and their applications across diverse domains.

Related papers

• Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
  multimodal CoT (MCoT) reasoning has recently garnered significant research attention. Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data. We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
  arXiv  Detail & Related papers  (2025-03-16T18:39:13Z)
• A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications [7.414857515253022]
  We introduce the basic design of MoE, including gating functions, expert networks, routing mechanisms, training strategies, and system design. We then explore the algorithm design of MoE in important machine learning paradigms such as continual learning, meta-learning, multi-task learning, and reinforcement learning.
  arXiv  Detail & Related papers  (2025-03-10T10:08:55Z)
• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs) We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• A Survey on Diffusion Models for Anomaly Detection [41.22298168457618]
  Diffusion models (DMs) have emerged as a powerful class of generative AI models.<n>DMAD offers promising solutions for identifying deviations in increasingly complex and high-dimensional data.
  arXiv  Detail & Related papers  (2025-01-20T12:06:54Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct [148.39859547619156]
  We propose MMEvol, a novel multimodal instruction data evolution framework.<n>MMEvol iteratively improves data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution.<n>Our approach reaches state-of-the-art (SOTA) performance in nine tasks using significantly less data compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-09T17:44:00Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• 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)
• The Revolution of Multimodal Large Language Models: A Survey [46.84953515670248]
  Multimodal Large Language Models (MLLMs) can seamlessly integrate visual and textual modalities. This paper provides a review of recent visual-based MLLMs, analyzing their architectural choices, multimodal alignment strategies, and training techniques.
  arXiv  Detail & Related papers  (2024-02-19T19:01:01Z)
• Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review [10.325003320290547]
  This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. This review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, and the phenomenon of model hallucinations.
  arXiv  Detail & Related papers  (2024-02-15T22:43:02Z)
• A Survey of Resource-efficient LLM and Multimodal Foundation Models [22.23967603206849]
  Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and multimodal models, are revolutionizing the entire machine learning lifecycle. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. This survey delves into the critical importance of such research, examining both algorithmic and systemic aspects.
  arXiv  Detail & Related papers  (2024-01-16T03:35:26Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)

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.