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arXiv Computer Vision By Jiahong Yuan, Weiming Mi, Tao Zhang, Haoyin Zhou

TEDi: Temporal Memory-Enhanced and Denoising Transformer for Surgical Instrument Segmentation

Read the original on arXiv Computer Vision →

TEDi is a Temporal memory-Enhanced and Denoising Transformer designed for surgical instrument segmentation. It introduces a query-level memory bank with a memory search enhancement encoder to incorporate discriminative representations from past frames, and a temporal consistency denoising module that builds a cross‑frame semantic anchor to stabilize predictions. Experiments on EndoVis 2017 and EndoVis 2018 show that TEDi outperforms existing state‑of‑the‑art methods, indicating its effectiveness for computer‑assisted surgery.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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