Interactive image segmentation aims at obtaining a segmentation mask for an image using simple user annotations. During each round of interaction, the segmentation result from the previous round serves as feedback to guide the user's annotation and provides dense prior information for the segmentation model, effectively acting as a bridge between interactions. Existing methods overlook the importance of feedback or simply concatenate it with the original input, leading to underutilization of feedback and an increase in the number of required annotations. To address this, we propose an approach called Focused and Collaborative Feedback Integration (FCFI) to fully exploit the feedback for click-based interactive image segmentation. FCFI first focuses on a local area around the new click and corrects the feedback based on the similarities of high-level features. It then alternately and collaboratively updates the feedback and deep features to integrate the feedback into the features. The efficacy and efficiency of FCFI were validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results show that FCFI achieved new state-of-the-art performance with less computational overhead than previous methods. The source code is available at https://github.com/veizgyauzgyauz/FCFI.

翻译：交互式图像分割旨在通过简单的用户标注获取图像的分割掩码。在每轮交互中，前一轮的分割结果作为反馈引导用户标注，并为分割模型提供密集先验信息，有效充当交互之间的桥梁。现有方法忽视了反馈的重要性，或仅将其与原始输入简单拼接，导致反馈利用不充分且所需标注次数增加。为解决这一问题，我们提出了一种名为“聚焦与协同反馈融合（FCFI）”的方法，以充分利用反馈进行基于点击的交互式图像分割。FCFI首先聚焦于新点击点周围的局部区域，基于高层特征的相似性校正反馈。随后，它交替协同更新反馈与深层特征，将反馈整合到特征中。在GrabCut、Berkeley、SBD和DAVIS四个基准数据集上的实验验证了FCFI的有效性和高效性。结果表明，FCFI以更低的计算开销实现了新的最优性能。源代码已开源至https://github.com/veizgyauzgyauz/FCFI。