SADe：用于弱支持注释的少样本分割的稀疏原子支持净化
SADe: Sparse-Atom Support Decontamination for Few-Shot Segmentation with Weak Support Annotations
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中文总结 AI 辅助
研究针对少样本分割中弱支持注释问题，提出SADe方法，通过稀疏自动编码器原子证据估计支持补丁可靠性，经轻量级路由器生成清理后的支持掩码，在多种设置下提升了少样本分割性能，证明原子证据的有效性。
中文摘要 AI 辅助
少样本分割（FSS）通常假设干净的像素级支持掩码，但实际的支持监督通常使用框、涂鸦、粗糙掩码或伪掩码。这些弱注释可能包括与目标一起的纹理相似的干扰物和背景上下文，在查询预测之前污染类原型或视觉提示。我们引入了SADe，这是一个与预测器无关的支持净化层，它在没有查询信息的情况下估计所选支持补丁的可靠性。SADe的核心是稀疏自动编码器（SAE）原子证据：密集相似性可能对目标和纹理相似的上下文都有响应，而弱支持区域内外的对比原子激活提供了因子级别的可靠性线索。一个轻量级路由器将原子证据与密集相似性和情节统计相结合，以预测补丁可靠性并生成清理后的支持掩码。该路由器在FSS-1000的合成弱支持情节上训练一次后，在所有目标评估中被冻结。生成的掩码支持独立预测，也可以通过原生支持接口提供给异构FSS模型，而无需改变查询端推理。在匹配的弱支持协议下，SADe在九个独立提示-shot组合中的六个中实现了最高的查询mIoU。使用相同的ProMi查询头，在紧密框下，它与SAM3派生的掩码的mIoU相差0.03以内，在box-r2和box-r4下分别比它们高出11.17和19.49分。作为一个插件，在四个冻结的下游模型和两个数据集的72个匹配框家族比较中，SADe在70个中比原始支持有改进。在点和涂鸦提示上，其平均性能仍接近相应的原始支持基线。消融和原子去除控制表明，原子证据贡献了超越密集相似性的可靠性信息。
英文摘要
Few-shot segmentation (FSS) commonly assumes clean pixel-level support masks, yet practical support supervision often uses boxes, scribbles, coarse masks, or pseudo-masks. These weak annotations may include texture-similar distractors and background context alongside the target, contaminating class prototypes or visual prompts before query prediction. We introduce SADe, a predictor-agnostic support decontamination layer that estimates the reliability of selected support patches without query information. Central to SADe is sparse autoencoder (SAE) atom evidence: dense similarity may respond to both target and texture-similar context, whereas contrasting atom activations inside and outside the weak-support region provides factor-level reliability cues. A lightweight router combines atom evidence with dense similarity and episode statistics to predict patch reliability and generate a cleaned support mask. Trained once on synthetic weak-support episodes from FSS-1000, the router is frozen for all target evaluations. The resulting mask supports standalone prediction or can be supplied to heterogeneous FSS models through native support interfaces without altering query-side inference. Under a matched weak-support protocol, SADe achieves the highest query mIoU in six of nine standalone prompt-shot combinations. With the same ProMi query head, it is within 0.03 mIoU of SAM3-derived masks under tight boxes and surpasses them by 11.17 and 19.49 points under box-r2 and box-r4, respectively. As a plug-in, SADe improves over raw support in 70 of 72 matched box-family comparisons across four frozen downstream models and two datasets. On point and scribble prompts, its average performance remains close to the corresponding raw-support baseline. Ablations and atom-removal controls show that atom evidence contributes reliability information beyond dense similarity.
