Add MPCount CVPR 2024 repository analysis and model pipeline documentation

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Documents the MPCount single domain generalization crowd counting model from CVPR 2024, providing technical overview and pipeline analysis.

Contents

Architecture Deep Dive

• VGG16-BN encoder-decoder with memory-augmented feature learning
• Memory attention mechanism: logits = (M^T @ Y) / √k; Y_new = M @ softmax(logits)
• Joint density estimation + classification head for crowd/non-crowd masking

Training Pipeline ('final' mode)

• Paired image training with consistency regularization on memory attention patterns
• Error-based masking: e_mask = (|IN(feat1) - IN(feat2)| < 0.5) selects reliable features
• Multi-task loss: L_total = L_den + 10×L_cls + 10×L_con

Key Innovations

• Learnable memory bank (256×1024) for domain-invariant prototypes
• Instance-normalized feature comparison for augmentation consistency
• Classification masking eliminates false positives in non-crowd regions

Performance
Cross-dataset generalization (train on A → test on B/Q):

• A→B: 11.2 MAE / 20.0 MSE
• A→Q: 112.8 MAE / 193.8 MSE

Includes

• Dataset preprocessing (ShanghaiTech A/B, UCF-QNRF)
• Model variants (base, mem, cls, final)
• Training/inference usage examples
• Deterministic implementation notes

File: MPCount_Summary.md (448 lines)

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