提出新型通道注意力机制，同时捕捉激活强度与空间分布特征。
ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition
- 用均值和负输入熵构成双特征描述符，感知通道激活的空间集中度。
- 在六个细粒度识别数据集上优于主流基线模型，提升显著。
- 轻量设计适配多种骨干网络，适合追求精度的视觉识别任务。
轻量级通道注意力机制广泛应用于图像分类，但在细粒度视觉识别（FGVR）中效果仍受限。现有模块多采用全局平均池化（GAP）对每个通道进行汇总，仅捕获激活幅度，忽略空间集中度，导致不同空间分布但均值相同的通道获得相同描述。本文提出浓度感知通道注意力（ConCA），将通道均值与一种平移不变的负输入熵（NegEnt）结合，后者通过在取反激活值上施加softmax计算得到，形成联合编码幅度与集中度的双特征描述符。该描述符由参数量随通道数线性增长的深度可分离1D卷积MLP映射为每通道权重。在六个细粒度基准测试中，ConCA在从零训练的受控协议下，优于无注意力、SE-Net及ECA-Net等基线，也超越四种更复杂的描述符方法；并在iNat2021-mini上跨八种骨干网络展现出良好泛化能力。结果表明，通道描述符及其映射至注意力权重的门控机制，是轻量通道注意力在FGVR中被低估的重要环节。
原文摘要 · Abstract (English)
Lightweight channel attention mechanisms are widely used in image classification, yet their effectiveness in fine-grained visual recognition (FGVR) remains limited. Most modules summarize each channel by global average pooling (GAP), which captures activation magnitude but ignores spatial concentration, so channels with different spatial distributions but identical means receive the same descriptor. We propose Concentration-Aware Channel Attention (ConCA), which pairs the mean with a shift-invariant negative-input entropy (NegEnt), computed via a softmax over the negated activations, forming a dual descriptor that jointly encodes magnitude and concentration. A depthwise 1-D convolutional multi-layer perceptron (MLP), whose parameter count is linear in the number of channels, maps the pair to a per-channel weight. On six fine-grained benchmarks, ConCA improves over attention-free, SE-Net, and ECA-Net baselines as well as four richer descriptor-based modules under a controlled from-scratch protocol, and it generalizes across eight backbones on iNat2021-mini. These results indicate that the channel descriptor, together with the per-channel gating that maps it to attention weights, is an important but underexplored aspect of lightweight channel attention in FGVR.
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