Leveraging Diffusion Models for Generalizable Dense Prediction

Core Concepts

The core message of this paper is that by reformulating the diffusion process as a deterministic mapping between input images and output prediction distributions, and using low-rank adaptation to fine-tune pre-trained text-to-image diffusion models, the proposed DMP approach can effectively leverage the inherent generalizability of diffusion models to perform various dense prediction tasks, such as 3D property estimation, semantic segmentation, and intrinsic image decomposition, even with limited training data in a specific domain.

Abstract

The paper introduces DMP (Diffusion Models as Priors), a method that leverages pre-trained text-to-image (T2I) diffusion models as a prior for generalizable dense prediction tasks. The key challenges addressed are the determinism-stochasticity misalignment between diffusion models and deterministic prediction tasks, as well as the need to strike a balance between learning target tasks and retaining the inherent generalizability of pre-trained T2I models.

To resolve the determinism-stochasticity issue, the authors reformulate the diffusion process as a chain of interpolations between input RGB images and their corresponding output signals, where the importance of input images gradually increases over the diffusion process. This allows the reverse diffusion process to become a series of deterministic transformations that progressively synthesize the desired output signals from input images.

To retain the generalizability of the pre-trained T2I model while learning target tasks, the authors use low-rank adaptation to fine-tune the pre-trained model with the aforementioned deterministic diffusion process for each dense prediction task.

The proposed DMP approach is evaluated on five dense prediction tasks: 3D property estimation (depth, normal), semantic segmentation, and intrinsic image decomposition (albedo, shading). The results show that with only a small amount of limited-domain training data (10K bedroom images), DMP can provide faithful estimations on in-domain and unseen images, outperforming existing state-of-the-art algorithms, especially on images where the off-the-shelf methods struggle.

Stats

The paper uses the following key metrics and figures:

For normal prediction: average L1 distance and average angular error
For monocular depth: average relative error (REL), percentage of pixels δ where max(yi/ˆyi, ˆyi/yi) < 1.25, and root mean square error (RMSE) of the relative depth
For semantic segmentation: intersection over union (IoU) and accuracy
For intrinsic image decomposition: mean square error (MSE)

Quotes

"Leveraging pre-trained T2I models as a prior for dense prediction is challenging for two reasons. First, most dense prediction tasks are inherently deterministic, posing difficulties when adapting a pre-trained T2I model designed for stochastic text-to-image generation. Second, it is crucial to strike a balance between learning target tasks and retaining the inherent generalizability of pre-trained T2I models."
"We show that with only a small amount of limited-domain training data (i.e., 10K bedroom images with labels), the proposed method can provide faithful estimations of the in-domain and unseen images, especially those that the existing SOTA algorithms struggle to handle effectively."

Key Insights Distilled From

Exploiting Diffusion Prior for Generalizable Dense Prediction

by Hsin-Ying Le... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2311.18832.pdf

Deeper Inquiries

How could the proposed DMP approach be extended to handle more diverse and complex dense prediction tasks beyond the five evaluated in the paper

The DMP approach can be extended to handle more diverse and complex dense prediction tasks by incorporating additional layers of abstraction and refinement in the diffusion process. One way to achieve this is by introducing hierarchical diffusion models that can capture multi-scale features and relationships in the input data. By cascading multiple diffusion steps at different levels of abstraction, the model can learn to generate more detailed and nuanced predictions for tasks such as fine-grained object segmentation, instance segmentation, or even scene understanding. Additionally, incorporating attention mechanisms or memory modules into the diffusion process can help the model focus on relevant regions of the input data, improving its ability to handle complex and varied prediction tasks.

What are the potential limitations or failure cases of the DMP approach, and how could they be addressed in future work

While the DMP approach shows promising results, there are potential limitations and failure cases that need to be addressed in future work. One limitation could be the scalability of the method to handle extremely large datasets or high-resolution images, as the computational complexity of the diffusion process may become prohibitive. Addressing this limitation would require exploring more efficient diffusion algorithms or parallel processing techniques. Additionally, the generalizability of the model to unseen domains or novel data distributions could be a challenge, especially if the pre-trained T2I model does not adequately capture the diversity of the target task data. To mitigate this, techniques such as domain adaptation, data augmentation, or meta-learning could be explored to improve the model's robustness and adaptability to new scenarios.

Given the success of leveraging pre-trained diffusion models as priors, how could similar techniques be applied to other computer vision tasks beyond dense prediction, such as image classification or object detection

The success of leveraging pre-trained diffusion models as priors for dense prediction tasks opens up opportunities to apply similar techniques to other computer vision tasks, such as image classification or object detection. For image classification, a pre-trained diffusion model could serve as a feature extractor to learn rich representations of input images, which can then be fed into a classifier for accurate classification. By fine-tuning the diffusion model on a specific classification task, the model can learn task-specific features that enhance classification performance. Similarly, for object detection, the pre-trained diffusion model can be used to extract features from input images, which can then be utilized by an object detection framework to localize and classify objects in the scene. This approach can improve the robustness and accuracy of object detection systems, especially in scenarios with limited training data.