Domain Adaptation Techniques for Camera-LLM Systems

EasyChair Preprint 15141

Abstract

Camera-Language Model (Camera-LLM) systems, which integrate visual data from cameras with language models, are crucial for a variety of applications, including real-time image captioning, object recognition, and interactive AI systems. However, these systems often face challenges due to domain shifts—variations in camera hardware, environmental conditions, and contextual changes in language. Domain adaptation techniques address this issue by enabling models to perform effectively across diverse domains despite differences in training and deployment environments.

This paper explores key domain adaptation techniques relevant to Camera-LLM systems. It covers data augmentation, feature alignment, adversarial training, transfer learning, and generative models. Additionally, it examines how these techniques mitigate the effects of variability in camera data and improve cross-modality alignment between visual inputs and language generation. The paper also discusses applications such as real-time captioning, object detection, and AR/VR, along with evaluation metrics to assess adaptation performance. The future directions point towards multi-domain adaptation, adaptive learning techniques, and human-in-the-loop systems. These advancements promise more robust and generalized Camera-LLM systems for real-world applications.

Keyphrases: Camera-Language Model (Camera-LLM), Domain Adaptation, Image Captioning, object detection

@booklet{EasyChair:15141,
  author    = {Docas Akinyele and Godwin Olaoye},
  title     = {Domain Adaptation Techniques for Camera-LLM Systems},
  howpublished = {EasyChair Preprint 15141},
  year      = {EasyChair, 2024}}