Reproduce “MHAFF: Multi-Head Attention Feature Fusion of CNN and Transformer for Cattle Identification” on OpenResearch
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About this paper
Rabin Dulal, Lihong Zheng, Muhammad Ashad Kabir
MHAFF combines local CNN and global transformer features for cattle muzzle identification using multi-head attention, with ViT features as query and value and ResNet features as key. It achieves validation accuracy of 99.88% on Cattle-1 and 99.52% on Cattle-2, exceeding addition and concatenation on both datasets. These results apply to validation splits and do not establish recognition of unseen cattle.
Key insights
- Multi-head attention can effectively learn and leverage contextual relationships between local features (from CNNs) and global features (from Transformers), creating a more discriminative representation.
- Deriving Query and Value from global (ViT-t) features and Key from local (Res-t) features optimizes the attention mechanism for comprehensive feature interaction in this specific task.
- The MHAFF approach leads to more focused and precise attention maps, indicating an improved ability to prioritize discriminative muzzle patterns over irrelevant background.
Summary by alphaXiv.
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