Currently, the generative model has garnered considerable attention due to
its application in addressing the challenge of scarcity of abnormal samples in
the industrial Internet of Things (IoT). However, challenges persist regarding
the edge deployment of generative models and the optimization of joint edge
AI-generated content (AIGC) tasks. In this paper, we focus on the edge
optimization of AIGC task execution and propose GMEL, a generative model-driven
industrial AIGC collaborative edge learning framework. This framework aims to
facilitate efficient few-shot learning by leveraging realistic sample synthesis
and edge-based optimization capabilities. First, a multi-task AIGC
computational offloading model is presented to ensure the efficient execution
of heterogeneous AIGC tasks on edge servers. Then, we propose an
attention-enhanced multi-agent reinforcement learning (AMARL) algorithm aimed
at refining offloading policies within the IoT system, thereby supporting
generative model-driven edge learning. Finally, our experimental results
demonstrate the effectiveness of the proposed algorithm in optimizing the total
system latency of the edge-based AIGC task completion.

提出了一种基于生成模型的工业 AIGC 协同边缘学习框架，通过利用真实样本合成和基于边缘的优化功能，实现高效的少样本学习，包括多任务 AIGC 计算卸载模型和注意力增强的多智能体强化学习算法，最终实现了边缘化 AIGC 任务完成的系统延迟优化。

基于多智能体强化学习的工业 AIGC 服务在无线边缘网络上的卸载

Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless  Edge Networks

Deep convolutional neural networks (CNNs) based approaches have achieved
great performance in video matting. Many of these methods can produce accurate
alpha estimation for the target body but typically yield fuzzy or incorrect
target edges. This is usually caused by the following reasons: 1) The current
methods always treat the target body and edge indiscriminately; 2) Target body
dominates the whole target with only a tiny proportion target edge. For the
first problem, we propose a CNN-based module that separately optimizes the
matting target body and edge (SOBE). And on this basis, we introduce a
real-time, trimap-free video matting method via progressively optimizing the
matting target body and edge (POBEVM) that is much lighter than previous
approaches and achieves significant improvements in the predicted target edge.
For the second problem, we propose an Edge-L1-Loss (ELL) function that enforces
our network on the matting target edge. Experiments demonstrate our method
outperforms prior trimap-free matting methods on both Distinctions-646 (D646)
and VideoMatte240K(VM) dataset, especially in edge optimization.

深度卷积神经网络在视频抠像中取得了很好的性能。该研究介绍了一种基于卷积神经网络的模块来分别优化抠像目标主体和边缘，以及一种实时、无参考标定的视频抠像方法，通过逐步优化抠像目标主体和边缘实现了显著的边缘优化，同时提出一种能够强化网络在抠像目标边缘上的损失函数。实验结果表明本方法在 Distinctions-646（D646）和 VideoMatte240K（VM）数据集上的性能优于现有的无参考标定抠像方法，尤其是在边缘优化方面。