Video　object　detection　(VOD)　is　a　sophisticated　visual　task.　It　is　a　consensus　that　is　used　to　find　effective　supportive　information　from　correlation　frames　to　boost　the　performance　of　the　model　in　VOD　tasks.　In　this　paper,　we　not　only　improve　the　method　of　finding　supportive　information　from　correlation　frames　but　also　strengthen　the　quality　of　the　features　extracted　from　the　correlation　frames　to　further　strengthen　the　fusion　of　correlation　frames　so　that　the　model　can　achieve　better　performance.　The　feature　refinement　module　FRM　in　our　model　refines　the　features　through　the　key–value　encoding　dictionary　based　on　the　even-order　Taylor　series,　and　the　refined　features　are　used　to　guide　the　fusion　of　features　at　different　stages.　In　the　stage　of　correlation　frame　fusion,　the　generative　MLP　is　applied　in　the　feature　aggregation　module　DFAM　to　fuse　the　refined　features　extracted　from　the　correlation　frames.　Experiments　adequately　demonstrate　the　effectiveness　of　our　proposed　approach.　Our　YOLOX-based　model　can　achieve　83.3%　AP50　on　the　ImageNet　VID　dataset.　©　2023　by　the　authors.