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- W3204643350 abstract "The ability to capture inter-frame dynamics has been critical to the development of video salient object detection (VSOD). While many works have achieved great success in this field, a deeper insight into its dynamic nature should be developed. In this work, we aim to answer the following questions: How can a model adjust itself to dynamic variations as well as perceive fine differences in the real-world environment; How are the temporal dynamics well introduced into spatial information over time? To this end, we propose a dynamic context-sensitive filtering network (DCFNet) equipped with a dynamic context-sensitive filtering module (DCFM) and an effective bidirectional dynamic fusion strategy. The proposed DCFM sheds new light on dynamic filter generation by extracting location-related affinities between consecutive frames. Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner. Experimental results demonstrate that our proposed method can achieve state-of-the-art performance on most VSOD datasets while ensuring a real-time speed of 28 fps. The source code is publicly available at https://github.com/OIPLab-DUT/DCFNet." @default.
- W3204643350 created "2021-10-11" @default.
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- W3204643350 date "2021-10-01" @default.
- W3204643350 modified "2023-10-10" @default.
- W3204643350 title "Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection" @default.
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- W3204643350 doi "https://doi.org/10.1109/iccv48922.2021.00158" @default.
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