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Better object segmentation of unknown objects using region awareness with RAML

Better object segmentation of unknown objects using region awareness with RAML


Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation



As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution (OOD) objects, especially under a few-shot condition.


... state-of-the-art ... Deep Metric Learning Network (DMLNet), relies on pixel-level metric learning ... identification of similar regions having different semantics is difficult.


... propose ... region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning.


... propose a novel meta-channel aggregation (MCA) module to further separate anomaly regions, forming high-quality sub-region candidates and thereby improving the model performance for OOD objects.


... conducted ... experiments and ablation studies on ... datasets for anomaly segmentation and .. for incremental few-shot learning.


... show that the proposed RAML achieves SOTA performance in both stages of open world segmentation. ...



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