It　is　important　to　extract　vehicle　appearance　features　for　vehicle　re-identification.　The　appearance　variation　of　the　same　vehicle　from　different　viewpoints　and　the　appearance　similarity　between　vehicles　from　different　classes　bring　challenges　for　capturing　the　descriptive　features.　Considering　these,　we　propose　a　multi-scale　attention　feature　fusion　network　(MSAF)　for　vehicle　re-identification.　It　uses　ResNet50　as　the　backbone,　and　introduces　a　scalable　channel　attention　module　for　each　feature　channel.　Then　a　multi-scale　fusion　module　is　designed　to　output　the　final　extracted　vehicle　features.　Experimental　results　on　the　VERI-Wild　dataset　indicate　that　the　proposed　MSAF　achieves　high　Rank-1　index　of　91.20%　with　mAP　of　80.20%.　©　2022　ACM.