ConCA: Concentration-Aware Channel Attention for Fine-Grained Visual Recognition
ConCA is proposed, 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.