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Found 2 papers, 2 papers with code
T-Mamba: Frequency-Enhanced Gated Long-Range Dependency for Tooth 3D CBCT Segmentation
To address this issue, we propose T-Mamba, integrating shared positional encoding and frequency-based features into vision mamba, to address limitations in spatial position preservation and feature enhancement in frequency domain.
GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data Synthesis
To segment glass surfaces with higher accuracy, we make full use of two visual foundation models: Segment Anything (SAM) and Stable Diffusion. Specifically, we devise a simple glass surface segmentor named GEM, which only consists of a SAM backbone, a simple feature pyramid, a discerning query selection module, and a mask decoder.
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