Weakly　supervised　video　object　segmentation　(WSVOS)　is　a　vital　yet　challenging　task　in　which　the　aim　is　to　segment　pixel-level　masks　with　only　category　labels.　Existing　methods　still　have　certain　limitations,　e.g.,　difficulty　in　comprehending　appropriate　spatiotemporal　knowledge　and　an　inability　to　explore　common　semantic　information　with　category　labels.　To　overcome　these　challenges,　we　formulate　a　novel　framework　by　integrating　multisource　saliency　and　incorporating　an　exemplar　mechanism　for　WSVOS.　Specifically,　we　propose　a　multisource　saliency　module　to　comprehend　spatiotemporal　knowledge　by　integrating　spatial　and　temporal　saliency　as　bottom-up　cues,　which　can　effectively　eliminate　disruptions　due　to　confusing　regions　and　identify　attractive　regions.　Moreover,　to　our　knowledge,　we　make　the　first　attempt　to　incorporate　an　exemplar　mechanism　into　WSVOS　by　proposing　an　adaptive　exemplar　module　to　process　top-down　cues,　which　can　provide　reliable　guidance　for　co-occurring　objects　in　intraclass　videos　and　identify　attentive　regions.　Our　framework,　which　comprises　the　two　aforementioned　modules,　offers　a　new　perspective　on　directly　constructing　the　correspondence　between　bottom-up　cues　and　top-down　cues　when　ground-truth　information　for　the　reference　frames　is　lacking.　Comprehensive　experiments　demonstrate　that　the　proposed　framework　achieves　state-of-the-art　performance.