Life-IQA：通过 GCN 增强的层间交互与基于 MoE 的特征解耦提升盲图像质量评价
Life-IQA: Boosting Blind Image Quality Assessment through GCN-enhanced Layer Interaction and MoE-based Feature Decoupling
用 GCN 增强的跨层交叉注意力替代普通 Transformer 解码器做质量特征解码，并用 MoE 按失真类型分专家解耦特征，这套「层间交互 + 专家解耦」的解码思路可迁移到其他需要融合骨干网络浅层与深层特征的回归/预测任务。
原文依据 utilizes the GCN-enhanced deepest-layer features as query and the penultimate-layer features as key, value, then performs cross-attention
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论盲图像质量评价（BIQA）在评估与优化视觉体验方面起着关键作用。现有大多数 BIQA 方法将骨干网络提取的浅层与深层特征进行融合，却忽视了它们对质量预测的不相等贡献。此外，尽管多种视觉编码器骨干网络已被广泛用于 BIQA，有效的质量解码架构仍缺乏充分探索。为解决上述局限，本文研究了浅层与深层特征对 BIQA 的贡献，并提出一种有效的质量特征解码框架，通过 GCN 增强的层间交互与基于 MoE 的特征解耦实现，称为 Life-IQA。具体而言，GCN 增强的层间交互模块以 GCN 增强的最深层特征作为查询、以倒数第二层特征作为键和值，随后执行交叉注意力以实现特征交互。此外，本文提出基于 MoE 的特征解耦模块，通过针对特定失真类型或质量维度专门化的不同专家对融合表示进行解耦。大量实验表明，与普通 Transformer 解码器相比，Life-IQA 在精度与开销之间取得了更有利的平衡，并在多个 BIQA 基准上达到最优性能。
英文原文摘要 1336 字符
Blind image quality assessment (BIQA) plays a crucial role in evaluating and optimizing visual experience. Most existing BIQA approaches fuse shallow and deep features extracted from backbone networks, while overlooking the unequal contributions to quality prediction. Moreover, while various vision encoder backbones are widely adopted in BIQA, the effective quality decoding architectures remain underexplored. To address these limitations, this paper investigates the contributions of shallow and deep features to BIQA, and proposes a effective quality feature decoding framework via GCN-enhanced \underline l ayer\underline i nteraction and MoE-based \underline f eature d\underline e coupling, termed (Life-IQA). Specifically, the GCN-enhanced layer interaction module utilizes the GCN-enhanced deepest-layer features as query and the penultimate-layer features as key, value, then performs cross-attention to achieve feature interaction. Moreover, a MoE-based feature decoupling module is proposed to decouple fused representations though different experts specialized for specific distortion types or quality dimensions. Extensive experiments demonstrate that Life-IQA shows more favorable balance between accuracy and cost than a vanilla Transformer decoder and achieves state-of-the-art performance on multiple BIQA benchmarks.