Remote zero-shot object recognition, i.e., offloading zero-shot object recognition task from one mobile device to remote mobile edge computing (MEC) server or another mobile device, has become a common and important task to solve for 6G. In order to tackle this problem, this paper first establishes a zero-shot multi-level feature extractor, which projects the image into visual, semantic, as well as intermediate feature space in a lightweight way. Then, this paper proposes a novel multi-level feature transmission framework powered by semantic knowledge base (SKB), and characterizes the semantic loss and required transmission latency at each level. Under this setup, this paper formulates the multi-level feature transmission optimization problem to minimize the semantic loss under the end-to-end latency constraint. The optimization problem, however, is a multi-choice knapsack problem, and thus very difficult to be optimized. To resolve this issue, this paper proposes an efficient algorithm based on convex concave procedure to find a high-quality solution. Numerical results show that the proposed design outperforms the benchmarks, and illustrate the tradeoff between the transmission latency and zero-shot classification accuracy, as well as the effects of the SKBs at both the transmitter and receiver on classification accuracy.

翻译：远程零样本目标识别（即将零样本目标识别任务从移动端卸载至移动边缘计算服务器或另一移动端）已成为第六代通信亟需解决的重要常见问题。为攻克该难题，本文首先构建轻量级零样本多级特征提取器，将图像映射至视觉、语义及中间特征空间。随后，本文提出基于语义知识库的创新多级特征传输框架，并刻画各层级语义损失与所需传输时延。在该框架下，本文构建面向端到端时延约束下语义损失最小化的多级特征传输优化问题。然而，该优化问题属于多选择背包问题，求解难度极大。为解决此挑战，本文提出基于凸凹过程的优化算法以获取高质量解。数值结果表明，所提方案性能显著优于基准方法，并揭示了传输时延与零样本分类精度间的权衡关系，以及收发两端语义知识库对分类精度的影响机制。