In　the　field　of　computer　vision，　visual　segmentation　is　a　fundamental　task　that　categorizes　pixels　in　an　image　or　video　frame　into　distinct　regions.　Thanks　to　the　significant　development　of　visual　segmentation　techniques，it　plays　a　key　role　in　various　applications　such　as　autonomous　driving，　aerial　remote　sensing，　and　video　scene　understanding.　In　recent　years，Transformer-based　visual　segmentation　has　attracted　much　attention　because　of　its　long-range　dependency　modeling　capability.　With　the　continuous　optimization　and　updating　of　Transformer’s　model　architecture，there　is　an　urgent　need　to　more　comprehensively　understand　and　recognize　the　existing　progress　and　development　trend　of　Transformer　in　field　of　visual　segmentation，　and　to　find　out　the　deficiencies　and　challenges，　so　as　to　explore　the　core　theory　of　Transformer　in　a　deeper　way.　To　this　end，　this　paper　organizes，　reviews，　analyzes　and　explores　the　recent　advances　in　Transformer-based　visual　segmentation　techniques　from　two　visual　pipelines　of　image/　video，　not　only　summarizing　the　theoretical　framework　of　Transformer，　but　also　giving　some　application　examples　and　research　hotspots，so　as　to　make　a　summary　and　overlook.　Specifically，the　background　of　the　Transformer　is　initially　reviewed，　including　problem　definition，　datasets，indicators，　and　the　basic　structure，　in　which　the　problem　definition　describes　the　expected　goals　and　results　of　visual　segmentation　in　image/video　tasks；the　dataset　and　indicators　respond　to　the　specific　application　scenarios　of　the　model　as　well　as　the　performance　measures；the　basic　structure　describes　the　core　modules　of　the　algorithm，　the　implementation　process，　and　the　relationship　between　the　individual　module.　Then，　the　four　methodologies　of　Transformer　are　highlighted　in　detail　in　terms　of　image　semantic　and　instance　segmentation，as　well　as　the　video　semantic　and　instance　segmentation，　and　current　research　hotspots　are　discussed.　For　the　task　of　image　semantic　segmentation，　the　representative　structures　of　Transformer　are　analyzed，including　pure　Transformer　and　dual-branch　structures，　and　the　motivation　and　application　effect　of　Transformer’s　improvement　are　exhibited　and　the　visual　results　are　shown　with　the　practical　application　cases　of　unpaved　road　segmentation　of　UAV　images　and　semantic　segmentation　of　remote　sensing　images，　while　image　instance　segmentation　summarizes　the　typical　structure　of　Transformer　without/with　end-to-end　framework.　Video　semantic　segmentation　is　mainly　categorized　into　accuracy-oriented　and　efficiency-oriented　Transformer　structures，　while　video　instance　segmentation　includes　frame-by-frame　and　segment-by-segment　Transformer　structure.　Notably，　video　instance　segmentation　takes　livestreaming　video　instance　segmentation　as　an　application　example，　and　not　only　discusses　the　available　datasets，　experimental　parameters　and　indicators，　but　also　evaluates　and　analyzes　the　performance　of　the　mainstream　methods　for　livestreaming　video　instance　segmentation，　and　shows　some　visual　results.　Subsequently，　for　segment　anything　（SAM），　open　vocabulary　segmentation，　and　referring　segmentation，　which　are　widely　concerned　in　the　field　of　visual　segmentation，　this　paper　traces　and　reviews　these　hotspots，　with　a　view　to　colliding　new　ideas　and　inspirations　in　visual　segmentation.　Finally，　although　Transformer-based　visual　segmentation　has　received　widespread　attention，　the　scientific　problems　have　gradually　emerged，　limiting　the　further　improvement　of　model　performance　and　efficiency.　Finally，　this　paper　summarizes　the　changeable　issues　that　still　need　to　be　addressed　in　terms　of　image/video　semantic/instance　segmentation　tasks　using　Transformer，　and　looks　forward　to　the　potential　future　development　directions　to　provide　some　insights　for　reference.　©　2024　Science　Press.　All　rights　reserved.