We investigate latent-space scalability for multi-task collaborative
intelligence, where one of the tasks is object detection and the other is input
reconstruction. In our proposed approach, part of the latent space can be
selectively decoded to support object detection while the remainder can be
decoded when input reconstruction is needed. Such an approach allows reduced
computational resources when only object detection is required, and this can be
achieved without reconstructing input pixels. By varying the scaling factors of
various terms in the training loss function, the system can be trained to
achieve various trade-offs between object detection accuracy and input
reconstruction quality. Experiments are conducted to demonstrate the adjustable
system performance on the two tasks compared to the relevant benchmarks.

本文研究多任务协作智能中潜空间可扩展性，其中一个任务是对象检测，另一个任务是输入重构。通过对训练损失函数各项的缩放因子进行变化，可以使系统在实现对象检测准确性和输入重构质量之间的权衡时得到调整，实验结果表明与相关基准相比，该系统在这两个任务上具有可调整的性能。