In this work, we present SEEM, a promotable and interactive model for segmenting
everything everywhere all at once in an image. In SEEM, we propose a novel and versatile …

Click-based interactive image segmentation aims at extracting objects with a limited user
clicking. A hierarchical backbone is the de-facto architecture for current methods. Recently …

Click-based interactive segmentation (IS) aims to extract the target objects under user
interaction. For this task, most of the current deep learning (DL)-based methods mainly …

Interactive image segmentation aims to segment the target from the background with the
manual guidance, which takes as input multimodal data such as images, clicks, scribbles …

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without
any training. We propose an adversarial masked contrastive painting (AMCP) process …

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 …

Interactive image segmentation (IIS) has emerged as a promising technique for decreasing
annotation time. Substantial progress has been made in pre-and post-processing for IIS, but …

Interactive image segmentation enables annotators to efficiently perform pixel-level
annotation for segmentation tasks. However, the existing interactive segmentation pipeline …

The goal of click-based interactive image segmentation is to extract target masks with the
input of positive/negative clicks. Every time a new click is placed, existing methods run the …

Interactive segmentation enables users to segment as needed by providing cues of objects,
which introduces human-computer interaction for many fields, such as image editing and …