Implicit and Explicit Language Guidance for Diffusion-based Visual Perception
This work addresses the challenge of leveraging diffusion models for perception tasks like segmentation and depth estimation, offering a novel approach that could benefit computer vision applications, though it appears incremental as it builds on existing diffusion and CLIP models.
The paper tackles the problem of adapting pre-trained text-to-image diffusion models for visual perception tasks by proposing an implicit and explicit language guidance framework (IEDP), which achieves a 2.2% improvement in mIoU for semantic segmentation and an 11.0% relative gain for depth estimation compared to baseline methods.
Text-to-image diffusion models have shown powerful ability on conditional image synthesis. With large-scale vision-language pre-training, diffusion models are able to generate high-quality images with rich texture and reasonable structure under different text prompts. However, it is an open problem to adapt the pre-trained diffusion model for visual perception. In this paper, we propose an implicit and explicit language guidance framework for diffusion-based perception, named IEDP. Our IEDP comprises an implicit language guidance branch and an explicit language guidance branch. The implicit branch employs frozen CLIP image encoder to directly generate implicit text embeddings that are fed to diffusion model, without using explicit text prompts. The explicit branch utilizes the ground-truth labels of corresponding images as text prompts to condition feature extraction of diffusion model. During training, we jointly train diffusion model by sharing the model weights of these two branches. As a result, implicit and explicit branches can jointly guide feature learning. During inference, we only employ implicit branch for final prediction, which does not require any ground-truth labels. Experiments are performed on two typical perception tasks, including semantic segmentation and depth estimation. Our IEDP achieves promising performance on both tasks. For semantic segmentation, our IEDP has the mIoU$^\text{ss}$ score of 55.9% on AD20K validation set, which outperforms the baseline method VPD by 2.2%. For depth estimation, our IEDP outperforms the baseline method VPD with a relative gain of 11.0%.