  We present LDP, a lightweight dense prediction neural architecture search
(NAS) framework. Starting from a pre-defined generic backbone, LDP applies the
novel Assisted Tabu Search for efficient architecture exploration. LDP is fast
and suitable for various dense estimation problems, unlike previous NAS methods
that are either computational demanding or deployed only for a single subtask.
The performance of LPD is evaluated on monocular depth estimation, semantic
segmentation, and image super-resolution tasks on diverse datasets, including
NYU-Depth-v2, KITTI, Cityscapes, COCO-stuff, DIV2K, Set5, Set14, BSD100,
Urban100. Experiments show that the proposed framework yields consistent
improvements on all tested dense prediction tasks, while being $5\%-315\%$ more
compact in terms of the number of model parameters than prior arts.
