Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models

• URL: http://arxiv.org/abs/2411.05005v1
• Date: Thu, 07 Nov 2024 18:59:53 GMT
• Title: Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models
• Authors: Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao, Martial Hebert, Yu-Xiong Wang,
• Abstract summary: We introduce a unified, versatile, diffusion-based framework, Diff-2-in-1, to handle both multi-modal data generation and dense visual perception. We further enhance discriminative visual perception via multi-modal generation, by utilizing the denoising network to create multi-modal data that mirror the distribution of the original training set.
• Score: 39.127620891450526
• License:
• Abstract: Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extractors. In contrast to these isolated and thus sub-optimal efforts, we introduce a unified, versatile, diffusion-based framework, Diff-2-in-1, that can simultaneously handle both multi-modal data generation and dense visual perception, through a unique exploitation of the diffusion-denoising process. Within this framework, we further enhance discriminative visual perception via multi-modal generation, by utilizing the denoising network to create multi-modal data that mirror the distribution of the original training set. Importantly, Diff-2-in-1 optimizes the utilization of the created diverse and faithful data by leveraging a novel self-improving learning mechanism. Comprehensive experimental evaluations validate the effectiveness of our framework, showcasing consistent performance improvements across various discriminative backbones and high-quality multi-modal data generation characterized by both realism and usefulness.

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