ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction

Abstract: While personalized text-to-image generation has enabled the learning of a single concept from multiple images, a more practical yet challenging scenario involves learning multiple concepts within a single image. However, existing works tackling this scenario heavily rely on extensive human annotations. In this paper, we introduce a novel task named Unsupervised Concept Extraction (UCE) that considers an unsupervised setting without any human knowledge of the concepts. Given an image that contains multiple concepts, the task aims to extract and recreate individual concepts solely relying on the existing knowledge from pretrained diffusion models. To achieve this, we present ConceptExpress that tackles UCE by unleashing the inherent capabilities of pretrained diffusion models in two aspects. Specifically, a concept localization approach automatically locates and disentangles salient concepts by leveraging spatial correspondence from diffusion self-attention; and based on the lookup association between a concept and a conceptual token, a concept-wise optimization process learns discriminative tokens that represent each individual concept. Finally, we establish an evaluation protocol tailored for the UCE task. Extensive experiments demonstrate that ConceptExpress is a promising solution to the UCE task. Our code and data are available at: https://github.com/haoosz/ConceptExpress

• The paper introduces ConceptExpress, a framework that leverages diffusion models to automatically extract and disentangle multiple image concepts from a single image without supervision.
• It employs a two-step method combining automatic latent concept localization via hierarchical clustering and filtering, followed by concept-wise masked denoising.
• The approach achieves superior concept similarity and classification accuracy on benchmarks like ImageNet, paving the way for advanced unsupervised visual analysis.

ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction

The paper "ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction" introduces a novel approach to disentangling and extracting multiple concepts from a single image without any supervision. The authors, Shaozhe Hao et al., present a methodology that leverages pretrained diffusion models, specifically Stable Diffusion, to address the challenge of Unsupervised Concept Extraction (UCE).

The paper articulates several critical contributions to the computer vision and machine learning communities. It outlines an innovative solution called ConceptExpress for the UCE task, which comprises two core components: automatic latent concept localization and concept-wise masked denoising.

Automatic Latent Concept Localization

Concept localization aims to autonomously locate and segment multiple salient concepts within an image. The authors utilize the spatial correspondence captured by self-attention maps in diffusion models to facilitate this task. The process is divided into three sequential steps:

1. Pre-clustering: Hierarchical clustering is applied to self-attention maps to identify latent concept masks.
2. Filtering: A filtering mechanism uses the cross-attention map of the end-of-text token to exclude non-salient backgrounds.
3. Post-clustering: The approach further refines the identified clusters by merging those with semantic similarities and removing backgrounds.

This process ensures that ConceptExpress can automatically determine the number of concepts and effectively segregate them without any human intervention.

Concept-wise Masked Denoising

In this phase, ConceptExpress utilizes a masked denoising loss to learn discriminative conceptual tokens for each identified concept. The innovative split-and-merge strategy is introduced to address the absence of initializers—a common pitfall in unsupervised settings. This strategy involves random initialization of multiple tokens per concept, optimizing these tokens partially, merging them, and continuing the training to robustly learn concept-specific features.

Additionally, the paper highlights the need for attention alignment to ensure the accurate association of learned tokens with the respective concepts. The alignment is regularized using earth mover's distance (EMD) to match the cross-attention maps accurately with self-attention-derived centroid maps.

Evaluation Protocol

The authors propose a comprehensive evaluation protocol tailored for UCE, which includes:

• Concept Similarity: Assessed using identity and compositional similarity metrics. This evaluation harnesses CLIP and DINO encoders to measure the similarity between generated and source concepts.
• Classification Accuracy: Measures the effectiveness of concept disentanglement by benchmarking classification accuracy based on concept prototypes from source images.

Experimental Results

ConceptExpress delivers superior performance compared to the adapted Break-A-Scene (BaS$^\dag$). It achieves higher scores in both concept similarity and classification accuracy across various evaluations, including ones conducted with ImageNet data.

Theoretical and Practical Implications

Practically, ConceptExpress paves the way for robust, unsupervised extraction of individual concepts from single images—a crucial capability for applications in personalized image generation, creative industries, and automated content creation. Theoretically, it demonstrates the extensive, untapped potential of diffusion models beyond mere text-to-image synthesis, as they inherently capture rich, distributive representations of visual concepts.

Future Directions

Potential advancements in this domain could target the following aspects:

1. Enhancing instance-level distinguishability to separate multiple similar instances within the same semantic category.
2. Addressing the challenge of accurately learning concepts from smaller regions by refining resolution and attention mechanisms.
3. Scaling ConceptExpress to handle larger, uncurated datasets effectively, bolstering its utility in real-world applications.

In summary, "ConceptExpress" offers significant contributions and innovations to the domain of unsupervised concept extraction, establishing a foundation for future explorations and practical implementations in AI-driven image generation.