📝 SummarXiv

Abstract

Conditional diffusion models have made impressive progress in the field of image processing, but the characteristics of constructing data distribution pathways make it difficult to exploit the intrinsic correlation between tasks in multi-task scenarios, which is even worse in ill-posed tasks with a lack of training data. In addition, traditional static condition control makes it difficult for networks to learn in multi-task scenarios with its dynamically evolving characteristics. To address these challenges, we propose a dynamic conditional double diffusion bridge training paradigm to build a general framework for ill-posed multi-tasks. Firstly, this paradigm decouples the diffusion and condition generation processes, avoiding the dependence of the diffusion model on supervised data in ill-posed tasks. Secondly, generated by the same noise schedule, dynamic conditions are used to gradually adjust their statistical characteristics, naturally embed time-related information, and reduce the difficulty of network learning. We analyze the learning objectives of the network under different conditional forms in the single-step denoising process and compare the changes in its attention weights in the network, demonstrating the superiority of our dynamic conditions. Taking dehazing and visible-infrared fusion as typical ill-posed multi-task scenarios, we achieve the best performance in multiple indicators on public datasets. The code has been publicly released at: https://anonymous.4open.science/r/DCDB-D3C2.