SOURCE-LINKED INTELLIGENCE

ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global