High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity

Generally, our main contributions can be summarized as follows:

• We propose DiffDIS, leveraging the powerful prior of diffusion models for the DIS task, elegantly navigating the traditional struggle to effectively balance the trade-off between receptive field expansion and detail preservation in traditional discriminative learning-based methods.

• We transform the recurrent nature of diffusion models into an end-to-end framework by implementing straightforward one-step denoising, significantly accelerating the inference speed.

• We introduce an auxiliary edge generation task, complemented by an effective interactive module, to achieve a nuanced balance in detail representation while also enhancing the determinism of the generated masks.

• We advance the field forward by outperforming almost all metrics on the DIS benchmark dataset, and thus establish a new SoTA in this space. Additionally, it boasts an inference speed that is orders of magnitude faster than traditional multi-step diffusion approaches without compromising accuracy.