Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperation
<|reference_start|>Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperation: Integrating low-light image enhancement techniques, in which diffusion-based AI-generated content (AIGC) models are promising, is necessary to enhance nighttime teleoperation. Remarkably, the AIGC model is computation-intensive, thus necessitating the allocation of AIGC tasks to edge servers with ample computational resources. Given the distinct cost of the AIGC model trained with varying-sized datasets and AIGC tasks possessing disparate demand, it is imperative to formulate a differential pricing strategy to optimize the utility of teleoperators and edge servers concurrently. Nonetheless, the pricing strategy formulation is under information asymmetry, i.e., the demand (e.g., the difficulty level of AIGC tasks and their distribution) of AIGC tasks is hidden information to edge servers. Additionally, manually assessing the difficulty level of AIGC tasks is tedious and unnecessary for teleoperators. To this end, we devise a framework of AIGC task allocation assisted by the Vision Language Model (VLM)-empowered contract theory, which includes two components: VLM-empowered difficulty assessment and contract theory-assisted AIGC task allocation. The first component enables automatic and accurate AIGC task difficulty assessment. The second component is capable of formulating the pricing strategy for edge servers under information asymmetry, thereby optimizing the utility of both edge servers and teleoperators. The simulation results demonstrated that our proposed framework can improve the average utility of teleoperators and edge servers by 10.88~12.43% and 1.4~2.17%, respectively. Code and data are available at https://github.com/ZiJun0819/VLM-Contract-Theory.<|reference_end|>
@article{zhan2024vision,
    title={Vision Language Model-Empowered Contract Theory for AIGC Task Allocation
  in Teleoperation},
    author={Zijun Zhan, Yaxian Dong, Yuqing Hu, Shuai Li, Shaohua Cao, and Zhu Han},
    journal={arXiv preprint arXiv:2407.17428},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17428},
    primaryClass={cs.CV cs.AI}
}