← 研踪 PaperTrailCVF · CVPR 2026:cvf:7d74b08ebf8be20e1a3b97325634028f · 记录 #1
ReMoE: Region-Mixture Experts for Adversarially-Robust Vision Transformers
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:38
摘要
Vision Transformers (ViTs) achieve state-of-the-art performance on a wide range of vision tasks, yet they remain highly vulnerable to adversarial perturbations due to the lack of explicit region-level semantic modeling. Adversarial perturbations are typically local and spatially structured, whereas the globally coupled self-attention and spatially uniform feed-forward networks in ViTs propagate local corruptions across the whole image without enforcing consistency within semantically coherent regions. To mitigate this mismatch, we propose Region-aware Mixture-of-Experts, namely "ReMoE", a plug-and-play module that replaces the standard feed-forward network (FFN) with a region-aware expert layer. Specifically, our ReMoE strategically introduces multi-granularity experts (i.e., global, center, and regional) and couples them with an attention-guided routing mechanism that operates on patch-to-region (P2R) and region-to-patch (R2P) transformations. This mechanism adaptively activates the most relevant experts for each spatial location according to its attention profile, enabling the model to capture region-level semantics and local context while preserving global consistency, thereby providing a stronger inductive bias for adversarially robust ViT representations. Extensive experiments demonstrate that our ReMoE substantially improves the adversarial robustness of ViTs with only marginal additional computational cost. Our code is available at https://github.com/zhongskr0114/ReMoE.
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记录 #1 · 2026 · 最新观测版本
ReMoE: Region-Mixture Experts for Adversarially-Robust Vision Transformers
Vision Transformers (ViTs) achieve state-of-the-art performance on a wide range of vision tasks, yet they remain highly vulnerable to adversarial perturbations due to the lack of explicit region-level semantic modeling. Adversarial perturbations are typically local and spatially structured, whereas the globally coupled self-attention and spatially uniform feed-forward networks in ViTs propagate local corruptions across the whole image without enforcing consistency within semantically coherent regions. To mitigate this mismatch, we propose Region-aware Mixture-of-Experts, namely "ReMoE", a plug-and-play module that replaces the standard feed-forward network (FFN) with a region-aware expert layer. Specifically, our ReMoE strategically introduces multi-granularity experts (i.e., global, center, and regional) and couples them with an attention-guided routing mechanism that operates on patch-to-region (P2R) and region-to-patch (R2P) transformations. This mechanism adaptively activates the most relevant experts for each spatial location according to its attention profile, enabling the model to capture region-level semantics and local context while preserving global consistency, thereby providing a stronger inductive bias for adversarially robust ViT representations. Extensive experiments demonstrate that our ReMoE substantially improves the adversarial robustness of ViTs with only marginal additional computational cost. Our code is available at https://github.com/zhongskr0114/ReMoE.
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