Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models
This work addresses the sub-optimal use of diffusion models in perception tasks for computer vision researchers, offering a more integrated approach.
The paper tackles the problem of using diffusion models for both image generation and dense visual perception by introducing Diff-2-in-1, a unified framework that enhances perception through multi-modal data generation and self-improving learning, resulting in consistent performance improvements across various backbones.
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.