Exploring the interaction between red,green,blue(RGB)and thermal infrared modalities is critical to the success of RGB-thermal(RGB-T)salient object detection(RGB-T SOD).In this paper,a cross-modal attention and reinfo...Exploring the interaction between red,green,blue(RGB)and thermal infrared modalities is critical to the success of RGB-thermal(RGB-T)salient object detection(RGB-T SOD).In this paper,a cross-modal attention and reinforcement network(CAR-Net)was proposed to explore the implicit relationship between the two modalities,which fully leverages the beneficial expression and complementary fusion of the two modalities.Specifically,CAR-Net has a cross-modal attention module(CAM)that enables efficient interaction and key information extraction through joint attention.It also includes a feature strengthener module(FSM)for improved representation using channel rank and loop methods.A large number of experiments show that the CAR-Net achieves the best performance on three publicly available datasets.展开更多
基金supported by the National Natural Science Foundation of China(62471124)the Heilongjiang Province Natural Science Foundation(LH2022F005)。
文摘Exploring the interaction between red,green,blue(RGB)and thermal infrared modalities is critical to the success of RGB-thermal(RGB-T)salient object detection(RGB-T SOD).In this paper,a cross-modal attention and reinforcement network(CAR-Net)was proposed to explore the implicit relationship between the two modalities,which fully leverages the beneficial expression and complementary fusion of the two modalities.Specifically,CAR-Net has a cross-modal attention module(CAM)that enables efficient interaction and key information extraction through joint attention.It also includes a feature strengthener module(FSM)for improved representation using channel rank and loop methods.A large number of experiments show that the CAR-Net achieves the best performance on three publicly available datasets.
