Context Tricks for Cheap Semantic Segmentation
This work provides incremental improvements for researchers and practitioners in computer vision by enhancing existing semantic segmentation methods with context tricks.
The paper tackles the challenge of achieving accurate and fast semantic segmentation by addressing bottlenecks in model complexity and small training datasets, proposing two simple context-based modifications that improve performance on MSRC-21 and PascalVOC-2010 benchmarks with minimal test-time slowdown.
Accurate semantic labeling of image pixels is difficult because intra-class variability is often greater than inter-class variability. In turn, fast semantic segmentation is hard because accurate models are usually too complicated to also run quickly at test-time. Our experience with building and running semantic segmentation systems has also shown a reasonably obvious bottleneck on model complexity, imposed by small training datasets. We therefore propose two simple complementary strategies that leverage context to give better semantic segmentation, while scaling up or down to train on different-sized datasets. As easy modifications for existing semantic segmentation algorithms, we introduce Decorrelated Semantic Texton Forests, and the Context Sensitive Image Level Prior. The proposed modifications are tested using a Semantic Texton Forest (STF) system, and the modifications are validated on two standard benchmark datasets, MSRC-21 and PascalVOC-2010. In Python based comparisons, our system is insignificantly slower than STF at test-time, yet produces superior semantic segmentations overall, with just push-button training.