Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
Yingling Lu, Yijun Yang, Zhaohu Xing, Qiong Wang, Lei Zhu

TL;DR
This paper introduces Diff-VPS, a diffusion-based multi-task network with adversarial temporal reasoning for improved video polyp segmentation, achieving state-of-the-art results by integrating high-level contextual information and temporal dependencies.
Contribution
The paper presents a novel diffusion model for video polyp segmentation that incorporates multi-task supervision and a temporal reasoning module with adversarial training, which are new contributions.
Findings
Achieves state-of-the-art performance on SUN-SEG dataset.
Effectively captures temporal dependencies and dynamic cues.
Enhances pixel-wise segmentation accuracy.
Abstract
Diffusion Probabilistic Models have recently attracted significant attention in the community of computer vision due to their outstanding performance. However, while a substantial amount of diffusion-based research has focused on generative tasks, no work introduces diffusion models to advance the results of polyp segmentation in videos, which is frequently challenged by polyps' high camouflage and redundant temporal cues.In this paper, we present a novel diffusion-based network for video polyp segmentation task, dubbed as Diff-VPS. We incorporate multi-task supervision into diffusion models to promote the discrimination of diffusion models on pixel-by-pixel segmentation. This integrates the contextual high-level information achieved by the joint classification and detection tasks. To explore the temporal dependency, Temporal Reasoning Module (TRM) is devised via reasoning and…
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Taxonomy
TopicsArtificial Intelligence Applications · Handwritten Text Recognition Techniques · Generative Adversarial Networks and Image Synthesis
MethodsSoftmax · Attention Is All You Need · Diffusion
