Underwater semantic segmentation is essential for marine ecosystem monitoring, yet remains challenging due to severe visual degradation. Light absorption and scattering often lead to color shifts, low contrast, and blurred boundaries, making shallow detail features unreliable. Existing underwater segmentation methods i...
Background Colorectal cancer is a leading cause of cancer-related mortality, and reliable polyp segmentation during colonoscopy is critical for early intervention. Existing deep learning segmentors often produce blurred boundaries and are sensitive to appearance variation across endoscopy devices. Methods We propose BA...
This work instantiates CHAINLSTM, a lightweight dual-task LSTM supporting per-event online detection and shows that chain-aware encoding shifts median prediction probability on path anomalies from 0.91 to 0.002, suggesting a wider separation margin for threshold-based detection.
Yi-Liu Xu, Ziwei Hong, Zhongheng Yang et al.· arXiv.org· 4 citations
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