Leveraging Implicit and Explicit Language Guidance to Enhance Diffusion-based Visual Perception

Core Concepts

The authors propose an implicit and explicit language guidance framework for diffusion-based visual perception, which jointly trains the diffusion model using implicit text embeddings generated from CLIP image encoder and explicit text embeddings from ground-truth labels, leading to superior performance on semantic segmentation and depth estimation tasks.

Abstract

The authors propose an implicit and explicit language guidance framework, named IEDP, for diffusion-based visual perception.
The key components are:
Implicit Language Guidance Branch:

Employs a frozen CLIP image encoder to directly generate implicit text embeddings, which are fed to the diffusion model to condition feature extraction.
This avoids the need for additional models to generate text prompts.
Explicit Language Guidance Branch:

Uses the ground-truth labels of training images as explicit text prompts, which are fed to a CLIP text encoder to generate text embeddings.
The ground-truth labels provide accurate class information to better guide feature learning during training.
During training, the two branches share model weights, allowing the implicit and explicit guidance to jointly train the diffusion model.
During inference, only the implicit branch is used, as the ground-truth labels are not available.
Experiments on semantic segmentation and depth estimation tasks show that the proposed IEDP outperforms previous diffusion-based methods. For example, on ADE20K semantic segmentation, IEDP achieves 55.9% mIoUss, outperforming the baseline VPD by 2.2%. On NYUv2 depth estimation, IEDP achieves 0.228 RMSE, outperforming VPD by 10.2% relatively.

Stats

The ADE20K dataset contains over 20,000 natural images with 150 semantic categories.
The NYUv2 dataset contains 24,000 training images and 645 test images of indoor scenes with depth maps.

Quotes

"Our IEDP comprises of 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."

Key Insights Distilled From

Implicit and Explicit Language Guidance for Diffusion-based Visual Perception

by Hefeng Wang,... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07600.pdf

Deeper Inquiries

How can the proposed implicit and explicit language guidance framework be extended to other vision-language tasks beyond visual perception

The proposed implicit and explicit language guidance framework can be extended to other vision-language tasks beyond visual perception by adapting the text-to-image diffusion model to suit the requirements of different tasks. For tasks like image captioning, the implicit branch can be modified to generate text embeddings that describe the content of the image in a natural language format. This can be achieved by training the model on image-caption pairs and using the implicit prompt module to extract relevant information from the image features. Similarly, for tasks like visual question answering, the explicit branch can be tailored to provide answers to questions based on the visual content, using ground-truth labels or generated captions as text prompts.

What are the potential limitations of the current approach, and how can it be further improved to handle more challenging scenarios

One potential limitation of the current approach is the reliance on frozen CLIP encoders for generating implicit and explicit text embeddings. While CLIP provides a strong foundation for connecting images and text, it may not capture all nuances and context-specific information required for complex vision-language tasks. To address this, the model could be enhanced by incorporating domain-specific language models or fine-tuning the CLIP encoders on task-specific data to improve the quality of text embeddings. Additionally, exploring more advanced techniques for feature extraction and conditioning, such as attention mechanisms or transformer architectures, could further enhance the model's performance in handling challenging scenarios.

Given the success of diffusion models in image synthesis, how can the insights from this work be leveraged to develop novel diffusion-based approaches for other dense prediction tasks, such as object detection or instance segmentation

Building on the success of diffusion models in image synthesis, novel diffusion-based approaches can be developed for other dense prediction tasks like object detection or instance segmentation. By leveraging the feature representation capabilities of diffusion models, these tasks can benefit from improved context-aware feature extraction and conditioning. For object detection, the diffusion model can be adapted to predict bounding boxes or object masks from noisy inputs, similar to the denoising process in image synthesis. For instance segmentation, the diffusion model can be used to segment individual instances by conditioning on image features and instance-specific information. By exploring different diffusion architectures and training strategies tailored to these tasks, significant advancements can be made in dense prediction tasks using diffusion-based approaches.