Recently,　the　Convolutional　Networks　(ConvNet)　has　become　the　dominated　approach　to　the　human　activity　classification　problem.　We　investigate　current　standard　ConvNet　architectures　and　pinpoint　one　of　their　main　limitations:　the　spatial-temporal　dependency　is　simply　captured　by　global　pooling　operation,　which　may　not　well　capture　the　complex　long　term　spatial-temporal　relationships　in　videos.　For　this　work,　we　propose　a　Spatial　Temporal　Attentional　Glimpse　(STAG)　module　to　overcome　this　shortcoming.　Specifically,　the　input　to　this　STAG　module　is　a　3D　tensor　which　is　first　processed　by　a　spatial-temporal　attention　block.　Spatial　Temporal　Glimpse　block　decomposes　the　resulting　tensor　into　two　low　dimensional　tensors　and　then　fuses　their　operation　results.　The　proposed　STAG　module　is　pluggable,　easy　to　learn,　and　effective　in　computation.　We　conduct　extended　ablation　studies　to　show　that　our　model　incorporated　with　the　STAG　block　substantially　improves　the　performance　over　the　state-of-the-art.　All　the　experimental　results,　the　trained　models,　and　the　complete　source　codes　will　be　released　to　facilitate　further　studies　on　this　problem.