Interactive Segmentation with Elaborate Focus Prior

Regional refinement for interactive segmentation is of great necessity to ensure the fidelity of segmented pixels nearby user-prompted locations, which specifies a local window ( focus view) for the latest click after a global prediction, where local pixels are revisited and optimized using numerous refining structures. Previous methods either utilize a two-stage pipeline to estimate the focus view or manually preset a fixed scope for all clicks, while the former is time-consuming, the latter fails to capture the correlation among click position, object geometry, and focus intensity. In this paper, we inherit the core idea of FCFI and dedicate a one-stage framework characterized with Elaborate Focus Prior (EFPNet). Concretely, EFPNet outputs an erroneous mask w.r.t historical feedback and newly-placed click in an end-to-end manner, which deduces precise focus region according to its max-connected component, followed with feedback correction considering image, feature and mask affinity. We further design a clicked-with-focus mechanism for efficient feedback integration. Extensive studies on four benchmarks have revealed outstanding performance of EFPNet for both efficacy and computational overhead.

Regional refinement for interactive segmentation is of great necessity to ensure the fidelity of segmented pixels nearby user-prompted locations, which specifies a local window (focus view) for the latest click after a global prediction, where local pixels are revisited and optimized using numerous refining structures. Previous methods either utilize a two-stage pipeline to estimate the focus view or manually preset a fixed scope for all clicks, while the former is time-consuming, the latter fails to capture the correlation among click position, object geometry, and focus intensity. In this paper, we inherit the core idea of FCFI and dedicate a one-stage framework characterized with Elaborate Focus Prior (EFPNet). Concretely, EFPNet outputs an erroneous mask w.r.t historical feedback and newly-placed click in an end-to-end manner, which deduces precise focus region according to its max-connected component, followed with feedback correction considering image, feature and mask affinity. We further design a clicked-with-focus mechanism for efficient feedback integration. Extensive studies on four benchmarks have revealed outstanding performance of EFPNet for both efficacy and computational overhead.