Double-click: dual-click iteration and dual-branch networks for interactive segmentation

The main aim of Interactive image segmentation is to accurately segment regions of interest by utilizing simple user-provided interactive information. Currently, the most popular training approach involves using the previous segmentation result as supplementary information for the next segmentation process to establish a connection between the two interactions. This iterative training method transfers all the information carried by interactions to the global level, without considering its impact at the local level. This will affect the final segmentation results and the training efficiency. To address this problem, we propose an iterative training strategy called Double-Click to involve cropping the image based on the newly added clicks and then separately feeding the cropped image and the newly added clicks into a smaller branch network to obtain predictions for the local region. Subsequently, the locally segmented result is fused with the previous segmentation result to achieve the final outcome. Then, the refined segmentation results are used to guide the next segmentation, ensuring that more precise results guide subsequent segmentations while maintaining the guidance information of the clicks on the global image. Our training strategy improves the training effectiveness by handling two clicks in an iterative process. The accuracy of the segmentation is ensured through both local and global predictions. Additionally, we designed a dual-branch network to accommodate our proposed training strategy. The effectiveness of this strategy has been tested on four validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results demonstrate that the Double-Click method achieves better performance with fewer interactions compared to previous methods.