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- W4285814469 abstract "Instance segmentation in road scenarios is more difficult because of the dense distribution of objects and severe occlusion among pedestrians and vehicles. To tackle the aforementioned issues, we take the popular single-stage instance segmentation framework CondInst as the baseline model and make improvements from the following three aspects: (1) introducing semantic and positional information into the mask head by concatenating the features from classification and regression head; (2) paying more attention to densely distributed objects by adopting a compactness loss in the segmentation task and increasing the loss weight of their samples; (3) proposing a novel strategy to select positive samples and depicting their qualities as the proportion of instance mask to the corresponding grid. We conduct comprehensive experiments on challenging datasets Cityscapes and BDD 100K to demonstrate the effectiveness of our proposed methods, and better results are obtained." @default.
- W4285814469 created "2022-07-19" @default.
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- W4285814469 date "2022-05-20" @default.
- W4285814469 modified "2023-10-18" @default.
- W4285814469 title "Segmenting against Dense Distribution and Occlusion in Road Scenarios" @default.
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- W4285814469 doi "https://doi.org/10.1109/cvidliccea56201.2022.9824938" @default.
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