ClickAdapter: Integrating Details Into Interactive Segmentation Model With Adapter

Click-based interactive segmentation is the most concise and widely used data labeling method. While existing interactive segmentation methods excel in handling simple targets, they encounter challenges in obtaining high-quality masks from some complex scenes, even with a large number of clicks. Also, the cost of retraining the model from scratch for special scenarios is unacceptably high. To address these issues, we propose ClickAdapter, a simple yet powerful interactive segmentation model adapter without the need for no pre-training. Through introducing a small number of additional parameters and computations, the adapter module effectively enhanced the ability of interactive segmentation models to obtain high-quality prediction with limited clicks. Specifically, we incorporate a detail extractor that aims to extract spatial correlations and local detail features of images. These fine-grained data are then integrated into a model with our adapter to generate segmentation masks with sharp and precise edges. During the training process, only the parameters of our adapter are learnable, thereby reducing the training cost. Features in special scenarios can also be infused more efficiently. To verify the efficiency and performance advantages of the proposed method, a series of experiments on a wide range of benchmarks were conducted, demonstrating that the proposed algorithm achieved cutting-edge performance compared to current state-of-the-art (SOTA) methods.