Advancing Image Multimodal Understanding in Large Language Models: Challenges, Techniques, and Future Directions

@article{1707494,
      author = {Vamsidhar Kamanuru},
      title = {Advancing Image Multimodal Understanding in Large Language Models: Challenges, Techniques, and Future Directions},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {954-959},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707494.pdf},
      abstract = {Recent advancements in large language models (LLMs) have expanded their capabilities beyond text processing to multimodal understanding, integrating image comprehension alongside textual reasoning. This development has unlocked new possibilities in artificial intelligence (AI), including improved human-computer interaction, automated content generation, and enhanced decision-making systems. However, achieving a seamless and efficient multimodal understanding remains a significant challenge due to issues such as data alignment, contextual consistency, computational efficiency, and generalization across diverse domains. This paper explores the key challenges in image multimodal understanding within LLMs, reviews state-of-the-art techniques for improving performance?such as vision-language pretraining, cross-modal fusion strategies, and advanced representation learning?and discusses promising future directions. By addressing these challenges and refining methodologies, we aim to pave the way for more robust and intelligent multimodal AI systems.},
      keywords = {Large Language Models, Multimodal Understanding, Image Processing, Vision-Language Models, Deep Learning, Cross-Modal Fusion, AI Research, Machine Learning, Representation Learning.},
      month = {March},
  }