arXiv:2412.04307v1 Announce Type: new
Abstract: Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessitates the exchange of intermediate information between model segments, with feature representations serving as crucial information carriers. To optimize information exchange, feature coding methods are applied to reduce transmission and storage overhead. Despite its importance, feature coding for large models remains an under-explored area. In this paper, we draw attention to large model feature coding and make three contributions to this field. First, we introduce a comprehensive dataset encompassing diverse features generated by three representative types of large models. Second, we establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. Third, we introduce two baseline methods derived from widely used image coding techniques and benchmark their performance on the proposed dataset. These contributions aim to advance the field of feature coding, facilitating more efficient large model deployment. All source code and the dataset will be made available on GitHub.
Feature Coding for Large Models: Advancements in Efficient Deployment
In recent years, large models have shown exceptional performance across various tasks, but they come with inherent challenges such as high computational costs and privacy concerns. As a result, distributed deployment has emerged as a potential solution, allowing for the efficient utilization of resources while addressing privacy concerns. However, this method requires the exchange of intermediate information between model segments, making feature representations crucial carriers of information.
Feature coding plays a vital role in optimizing information exchange by reducing transmission and storage overhead. Despite its importance, feature coding for large models remains a relatively under-explored area. In this paper, we shed light on the significance of feature coding for large models and make three key contributions to this field.
Comprehensive Dataset and Unified Test Conditions
We begin by introducing a comprehensive dataset that encompasses diverse features generated by three representative types of large models. This dataset serves as a valuable resource for researchers and practitioners in understanding the characteristics and properties of features in large models.
We also establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. This standardization is essential in promoting reproducibility and ensuring that advancements in feature coding can be accurately assessed and benchmarked against existing approaches.
Baseline Methods and Performance Evaluation
To kickstart advancements in feature coding for large models, we introduce two baseline methods derived from widely used image coding techniques. These methods provide a starting point for researchers to explore and develop more sophisticated feature coding approaches.
We benchmark the performance of these baseline methods on the proposed comprehensive dataset, allowing for comparative analysis. Through this evaluation, we aim to provide insights into the strengths and limitations of existing feature coding techniques while paving the way for further enhancements.
Multi-Disciplinary Nature and Relation to Multimedia Information Systems
The concepts and advancements in feature coding for large models have a multi-disciplinary nature and are closely related to the wider field of multimedia information systems. Multimedia information systems deal with the processing, storage, retrieval, and transmission of multimedia data, including text, images, videos, and audio.
Large models, animations, artificial reality, augmented reality, and virtual realities are all integral components of multimedia information systems. Feature coding techniques play a crucial role in optimizing the transmission and storage of these diverse multimedia data, enabling more efficient and effective deployment of large models in various applications.
By addressing the challenges and limitations of feature coding for large models, we can unlock new possibilities for multimedia information systems, allowing for more seamless integration of advanced technologies and richer user experiences.
In summary, this paper highlights the significance of feature coding for large models and presents valuable contributions to this under-explored area. The introduced comprehensive dataset, unified test conditions, and baseline methods open doors for further research, development, and advancements in feature coding. The multi-disciplinary nature of these concepts reinforces their relation to multimedia information systems, expanding the horizons of animations, artificial reality, augmented reality, and virtual realities.