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Unmanned Aerial Vehicle (UAV) has seen a dramatic rise in popularity for remote-sensing image acquisition and analysis in recent years. It has brought promising results in low-altitude monitoring tasks that require detailed visual inspections. Semantic segmentation is one of the hot topics in UAV remote sensing image analysis, as its capability to mine contextual semantic information from UAV images is crucial for achieving a fine-grained understanding of scenes. However, in the remote sensing field, recent reviews have not focused on combining "UAV remote sensing" and "semantic segmentation" to summarize the advanced works and future trends. In this study, we focus primarily on describing various recent semantic segmentation methods applied in UAV remote sensing images and summarizing their advantages and limitations. According to the distinction in modeling contextual semantic information, we have categorized and outlined the methods based on graph-based contextual models and deep-learning-based models. Publicly available UAV-based image datasets are also gathered to encourage systematic research on advanced semantic segmentation methods. We provide quantitative results of representative methods on two high-resolution UAV-based image datasets for fair comparisons and discussions in terms of semantic segmentation accuracy and model inference efficiency. Besides, this paper concludes some remaining challenges and future directions in semantic segmentation for UAV remote sensing images and points out that methods based on deep learning will become the future research trend. • Summarize semantic segmentation methods and datasets for UAV remote sensing images. • Provide fair comparisons of representative methods in accuracy and efficiency. • Identify challenges and future directions for UAV image semantic segmentation. • Semantic segmentation methods based on deep learning will be the mainstream. [ABSTRACT FROM AUTHOR]

Colorectal polyps are known to be potential precursors to colorectal cancer. Effective polyp segmentation during colonoscopy examinations can help the clinicians accurately locate potential polyp areas, and reduce misdiagnosis and missed diagnosis. Although existing approaches have achieved significant breakthroughs in medical image segmentation, polyp segmentation is still far from being solved. This is mainly due to the following reasons: (1) most of them tend to ignore the feature misalignment issues during the feature aggregation process; and (2) few algorithms explicitly consider the impact of boundary information on the performance of polyp segmentation. To solve the above issues, we formulate a novel neural network for polyp segmentation in endoscopy images. Different from existing approaches, we explore a new paradigm to enhance multi-level feature fusion by introducing the Feature Fusion Module, which leverages the semantic offset field learned to align the multi-level feature maps for resolving the feature misalignment issue. In addition, we design an auxiliary boundary branch to focus on boundary-aware information, and thus boost the performance of boundary prediction. Specifically, the reference boundary map learned through end-to-end optimization can be considered as a complementary feature of the high-level semantics representation, and then integrated into the main branch via the Boundary Embedding Module, which in turn promotes further refinement of the prediction, especially the boundary. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in polyp segmentation. [ABSTRACT FROM AUTHOR]