You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space - pixels, segments or group of segments. It is well known that each quantisation has its fair share of pros and cons; and the existence of a common optimal quantisation level suitable for all object categories is highly unlikely. Motivated by this observation, we propose a hierarchical random field model, that allows integration of features computed at different levels of the quantisation hierarchy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalises much of the previous work based on pixels or segments. We evaluate its efficiency on some of the most challenging data-sets for object class segmentation, and show it obtains state-of-the-art results.
CRFs are used in many semantic segmentation algorithms to regularize output labels. The Hierarchical Conditional Random Field (HCRF) [1] uses different levels of quantization, from pixels to segments. They operate under the assumption that there is unlikely to be a single optimal quantization level that is suitable for all object categories.