  The success of transformers in computer vision has led to several attempts to
adapt them for mobile devices, but their performance remains unsatisfactory in
some real-world applications. To address this issue, we propose PP-MobileSeg, a
semantic segmentation model that achieves state-of-the-art performance on
mobile devices. PP-MobileSeg comprises three novel parts: the StrideFormer
backbone, the Aggregated Attention Module (AAM), and the Valid Interpolate
Module (VIM). The four-stage StrideFormer backbone is built with MV3 blocks and
strided SEA attention, and it is able to extract rich semantic and detailed
features with minimal parameter overhead. The AAM first filters the detailed
features through semantic feature ensemble voting and then combines them with
semantic features to enhance the semantic information. Furthermore, we proposed
VIM to upsample the downsampled feature to the resolution of the input image.
It significantly reduces model latency by only interpolating classes present in
the final prediction, which is the most significant contributor to overall
model latency. Extensive experiments show that PP-MobileSeg achieves a superior
tradeoff between accuracy, model size, and latency compared to other methods.
On the ADE20K dataset, PP-MobileSeg achieves 1.57% higher accuracy in mIoU than
SeaFormer-Base with 32.9% fewer parameters and 42.3% faster acceleration on
Qualcomm Snapdragon 855. Source codes are available at
https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.8.
