  Semantic segmentation for scene understanding is nowadays widely demanded,
raising significant challenges for the algorithm efficiency, especially its
applications on resource-limited platforms. Current segmentation models are
trained and evaluated on massive high-resolution scene images ("data level")
and suffer from the expensive computation arising from the required multi-scale
aggregation("network level"). In both folds, the computational and energy costs
in training and inference are notable due to the often desired large input
resolutions and heavy computational burden of segmentation models. To this end,
we propose DANCE, general automated DAta-Network Co-optimization for Efficient
segmentation model training and inference. Distinct from existing efficient
segmentation approaches that focus merely on light-weight network design, DANCE
distinguishes itself as an automated simultaneous data-network co-optimization
via both input data manipulation and network architecture slimming.
Specifically, DANCE integrates automated data slimming which adaptively
downsamples/drops input images and controls their corresponding contribution to
the training loss guided by the images' spatial complexity. Such a downsampling
operation, in addition to slimming down the cost associated with the input size
directly, also shrinks the dynamic range of input object and context scales,
therefore motivating us to also adaptively slim the network to match the
downsampled data. Extensive experiments and ablating studies (on four SOTA
segmentation models with three popular segmentation datasets under two training
settings) demonstrate that DANCE can achieve "all-win" towards efficient
segmentation(reduced training cost, less expensive inference, and better mean
Intersection-over-Union (mIoU)).
