The　preceding　person　re-identification　methods　were　mostly　focused　on　the　learning　of　the　image　attention　region,　but　ignored　the　impact　of　the　non-attention　region　on　the　final　feature　learning.　If　the　feature　learning　of　image　non-attention　regions　is　enhanced　while　focusing　on　attention　regions,　the　final　person　features　can　be　further　enriched,　which　is　beneficial　to　the　accurate　identification　of　person　identity　information.　Based　on　this,　this　paper　proposed　a　visual　information　accumulation　network　(VIA　Net),　adopting　two　branches.　One　branch　tended　to　learn　the　global　features　of　the　image,　and　the　other　branch　was　expanded　into　a　multi-branch　structure.　By　combining　the　features　of　the　attention　and　non-attention　regions,　the　learning　of　local　features　could　be　gradually　strengthened,　thus　realizing　the　accumulation　of　visual　information　and　further　enriching　the　feature　information.　The　experimental　results　show　that　the　proposed　VIA　Net　could　attain　high　experimental　performance　in　terms　of　person　re-identification　datasets　such　as　Market-1501.　At　the　same　time,　the　experiment　on　the　In-Shop　Clothes　Retrieval　dataset　shows　that　the　network　could　also　be　applicable　to　general　image　retrieval　tasks　and　possess　certain　universality.　©　2022,　Editorial　of　Board　of　Journal　of　Graphics.　All　rights　reserved.