23 research outputs found
Graph-guided Architecture Search for Real-time Semantic Segmentation
Designing a lightweight semantic segmentation network often requires
researchers to find a trade-off between performance and speed, which is always
empirical due to the limited interpretability of neural networks. In order to
release researchers from these tedious mechanical trials, we propose a
Graph-guided Architecture Search (GAS) pipeline to automatically search
real-time semantic segmentation networks. Unlike previous works that use a
simplified search space and stack a repeatable cell to form a network, we
introduce a novel search mechanism with new search space where a lightweight
model can be effectively explored through the cell-level diversity and
latencyoriented constraint. Specifically, to produce the cell-level diversity,
the cell-sharing constraint is eliminated through the cell-independent manner.
Then a graph convolution network (GCN) is seamlessly integrated as a
communication mechanism between cells. Finally, a latency-oriented constraint
is endowed into the search process to balance the speed and performance.
Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS
achieves the new state-of-the-art trade-off between accuracy and speed. In
particular, on Cityscapes dataset, GAS achieves the new best performance of
73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202
Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search
Neural Architecture Search (NAS) has shown great potentials in automatically
designing neural network architectures for real-time semantic segmentation.
Unlike previous works that utilize a simplified search space with cell-sharing
way, we introduce a new search space where a lightweight model can be more
effectively searched by replacing the cell-sharing manner with cell-independent
one. Based on this, the communication of local to global information is
achieved through two well-designed modules. For local information exchange, a
graph convolutional network (GCN) guided module is seamlessly integrated as a
communication deliver between cells. For global information aggregation, we
propose a novel dense-connected fusion module (cell) which aggregates
long-range multi-level features in the network automatically. In addition, a
latency-oriented constraint is endowed into the search process to balance the
accuracy and latency. We name the proposed framework as Local-to-Global
Information Communication Network Search (LGCNet). Extensive experiments on
Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new
state-of-the-art trade-off between accuracy and speed. In particular, on
Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU
with the speed of 115.2 FPS on Titan Xp.Comment: arXiv admin note: text overlap with arXiv:1909.0679