MagFormer:- Parameter-Efficient Modular Approach for Person Re-Identification

Summary

• MagFormer, a modular transformer model, improves person re-identification by integrating image-to-image interactions during training to reduce noisy or unstable representations.
• It introduces three components(1)MALAA: Approximates dense relations using magnitude-aware landmarks. (2)RNS: Focuses attention on contextually relevant samples by sparsifying it. (3)DiffAttn: Cancels residual noise to boost identity consistency.
• MagFormer is scalable, interpretable, and consistently outperforms baselines.