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arXiv Computer Vision By Chen Chen, Chenggang Wu, Yongbiao Chen, Weidong Qiu, Zhengwei Qi, Haibing Guan

Channel-Level Mask Guidance for Slimmable Image Compression

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The paper introduces a channel‑level mask‑guided slimmable image compression technique that allows non‑consecutive channel selection for narrow models, improving performance at low bitrates. After training, a Mask‑to‑Slimmable (M2S) conversion turns the model into a standard slimmable network, enabling efficient inference without architectural changes. Variable‑rate control is achieved through lambda‑ and width‑conditioned modulation, quantization‑step scaling, and a freeze‑then‑fine‑tune training pipeline, yielding superior rate‑distortion performance and significant storage savings across multiple backbones.

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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