Language-Informed Visual Concept Extraction and Recomposition

핵심 개념

The core message of this paper is to learn a language-informed visual concept representation by distilling from pre-trained text-to-image generation models, enabling the extraction and recomposition of disentangled visual concepts along various concept axes.

초록

The paper proposes a framework for learning a language-informed visual concept representation by distilling from pre-trained text-to-image (T2I) generation models. The key ideas are:

Visual Concept Encoding by Inverting T2I Generation:

Train a set of concept encoders {fk(·)} to extract concept embeddings {ek} along different concept axes (e.g., category, color, material) from input images.
The concept encoders are trained to reproduce the input image using a pre-trained T2I model, given an axis-informed text template.
This allows the concept embeddings to be shared across instances and capture common visual characteristics for each concept axis.

Concept Disentanglement using Text Anchors:

To encourage better disentanglement of the concept embeddings, they are anchored to text embeddings obtained from a pre-trained Visual Question Answering (VQA) model.
This exploits the disentangled nature of linguistic concepts to improve the disentanglement of the visual concepts.

Concept Recomposition and Generalization:

At inference, the trained concept encoders can extract disentangled concept embeddings from test images.
These embeddings can be remixed to generate new images with novel concept compositions.
With a lightweight test-time finetuning, the encoders can also generalize to novel concepts unseen during training.

The experiments show that the proposed method outperforms existing text-based image editing baselines in terms of both quantitative metrics and human evaluation, demonstrating the effectiveness of the learned disentangled visual concept representation.

통계

"Our understanding of the visual world is centered around various concept axes, characterizing different aspects of visual entities."
"While different concept axes can be easily specified by language, e.g., color, the exact visual nuances along each axis often exceed the limitations of linguistic articulations, e.g., a particular style of painting."

인용구

"To facilitate efficient reasoning and communication of these concepts, humans created symbolic depictions that have evolved into natural language."
"Such natural language grounding of visual data has been instrumental in the recent proliferation of powerful large vision-language models that are capable of semantically identifying objects in images or generating photo-realistic images from arbitrary text prompts."

핵심 통찰 요약

Language-Informed Visual Concept Learning

by Sharon Lee,Y... 게시일 arxiv.org 04-04-2024

https://arxiv.org/pdf/2312.03587.pdf

더 깊은 질문

How can the proposed framework be extended to handle more complex and open-ended visual concepts beyond the predefined axes

The proposed framework can be extended to handle more complex and open-ended visual concepts beyond the predefined axes by incorporating a few key strategies. One approach could involve introducing a more dynamic and adaptive concept encoder architecture that can learn and extract a broader range of visual concepts. This could involve incorporating additional concept axes, allowing for a more comprehensive representation of visual entities. Additionally, leveraging unsupervised learning techniques such as self-supervised learning or reinforcement learning could enable the model to discover and encode more nuanced and abstract visual concepts. By training the model on a more diverse and extensive dataset that covers a wide range of visual concepts, the framework can be enhanced to handle complex and open-ended visual concepts effectively.

What are the potential limitations of using pre-trained VQA models as anchors for disentangling visual concepts, and how can this be further improved

While using pre-trained VQA models as anchors for disentangling visual concepts can be beneficial, there are potential limitations to consider. One limitation is the reliance on the accuracy and generalization capabilities of the VQA model. If the VQA model is biased or limited in its understanding of visual concepts, it may introduce biases or inaccuracies into the concept embeddings. To address this, it is essential to continuously update and fine-tune the VQA model on diverse and representative datasets to improve its performance in anchoring visual concepts. Additionally, incorporating multiple VQA models or ensemble methods can help mitigate the limitations of individual models and enhance the robustness of the disentanglement process.

Given the success of the language-informed visual concept representation, how can it be leveraged to enable more intuitive and expressive image editing interfaces for end-users

The success of the language-informed visual concept representation can be leveraged to enable more intuitive and expressive image editing interfaces for end-users by integrating it into user-friendly and interactive editing tools. By incorporating the disentangled visual concept embeddings into user interfaces, users can manipulate and edit images based on specific visual concepts such as category, color, style, and more. This can enable users to easily modify and customize images by simply selecting and adjusting the desired visual concepts through a user-friendly interface. Additionally, incorporating natural language processing capabilities can further enhance the user experience by allowing users to input text instructions or descriptions for image editing, making the process more intuitive and expressive. By integrating the language-informed visual concept representation into user-friendly editing interfaces, end-users can have a more seamless and creative editing experience.