Group　activity　recognition　is　a　challenging　task　that　involves　multiple　moving　actors　within　a　cluttered　scene.　Existing　methods　often　rely　on　object　detector　to　avoid　individual　bounding　box　labeling　during　testing,　but　are　prone　to　false　detections　due　to　factors　such　as　occlusion　and　background　clutter.　In　addition,　existing　detector-free　method　based　on　Transformer　attends　to　attention　map　that　is　too　sparse,　resulting　in　the　loss　of　some　important　foreground　information.　In　this　paper,　we　introduce　foreground-background　contrast　loss　(FB-Loss)　to　help　accurately　seek　discriminative　cues　in　the　foreground　and　eliminate　noise　interference　in　the　background.　Neither　ground-truth　bounding　boxes　nor　object　detectors　are　required　during　both　training　and　testing.　Experimental　results　on　public　datasets　show　that　our　proposed　method　achieves　the　state-of-the-art　performance.　©　2024　IEEE.