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UniGS: Unified Representation for Image Generation and Segmentation (2312.01985v1)

Published 4 Dec 2023 in cs.CV

Abstract: This paper introduces a novel unified representation of diffusion models for image generation and segmentation. Specifically, we use a colormap to represent entity-level masks, addressing the challenge of varying entity numbers while aligning the representation closely with the image RGB domain. Two novel modules, including the location-aware color palette and progressive dichotomy module, are proposed to support our mask representation. On the one hand, a location-aware palette guarantees the colors' consistency to entities' locations. On the other hand, the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers. To tackle the issue of lacking large-scale segmentation training data, we employ an inpainting pipeline and then improve the flexibility of diffusion models across various tasks, including inpainting, image synthesis, referring segmentation, and entity segmentation. Comprehensive experiments validate the efficiency of our approach, demonstrating comparable segmentation mask quality to state-of-the-art and adaptability to multiple tasks. The code will be released at \href{https://github.com/qqlu/Entity}{https://github.com/qqlu/Entity}.

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Citations (10)
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Summary

  • The paper introduces a unified framework that integrates image generation and segmentation using diffusion models and novel mask representation modules.
  • It employs a location-aware color palette and a progressive dichotomy module to ensure consistent and precise segmentation masks.
  • Experimental results demonstrate state-of-the-art image fidelity and segmentation accuracy, validated by metrics such as FID, CLIP, IoU, and recall.

Overview of UniGS: Unified Representation for Image Generation and Segmentation

The paper introduces UniGS, a framework that integrates image generation and segmentation into a unified representation via diffusion models. This is achieved by representing segmentation masks as colormaps, which aligns their representation more closely with RGB images. The goal is to address challenges such as varying numbers of entities while maintaining coherence between image and mask generation.

Technical Contributions

UniGS's framework proposes two novel modules to support the mask representation:

  1. Location-aware Color Palette: This module ensures that colors remain consistent with the entities' locations, facilitating discrimination among similar categories. It employs a grid-based approach, assigning each entity a fixed color based on its center-of-mass location to manage the complexity of distinguishing entities.
  2. Progressive Dichotomy Module (PDM): This module converts the colormap into high-quality entity-level masks via a depth-first binary search, without needing prior knowledge of the cluster numbers. PDM uses a pixel feature space combining RGB and LAB values to enhance segmentation accuracy, addressing issues like boundary noise and similar color differentiation.

Methodology and Pipeline

UniGS utilizes an inpainting pipeline to counter the lack of large-scale segmentation datasets, allowing flexibility across various tasks, such as inpainting, image synthesis, referring segmentation, and entity segmentation. This methodology enables the model to focus on specific regions, leveraging diverse segmentation datasets more efficiently.

A unified architecture based on latent diffusion models processes both image and mask generation flexibly, reducing computational demands through latent code operations. The framework's innovations allow for multitasking capabilities, improving both image fidelity and mask clarity.

Experimental Results

Extensive experiments demonstrate UniGS's efficiency, showcasing comparable segmentation quality to state-of-the-art models. The framework exhibits robust performance in both image quality (as measured by FID and CLIP scores) and segmentation accuracy (assessed through IoU and recall).

  • Inpainting and Image Synthesis: The results indicate significant improvements in integrating objects into scenes accurately, even capturing subtle features like shadows, showcasing UniGS's advanced understanding of spatial and textural contexts.
  • Referring and Entity Segmentation: Without explicit segmentation losses, UniGS achieved notable levels of segmentation accuracy, emphasizing its ability to align generated content with intended designs.

Implications and Future Directions

The development of UniGS presents significant implications for AI research, particularly in enhancing the coherence and realism of synthesized images. By unifying generation and segmentation within a single framework, UniGS has the potential to inspire new approaches in foundational models for dense prediction tasks.

Future directions involve exploring further integration of multiple tasks into singular models, improving efficiency and practicality within real-world applications. Additionally, extending the UniGS framework to other domains, such as video and 3D generation, may provide further opportunities for innovation.

Overall, UniGS represents a pivotal step towards more cohesive AI-generated content, bridging the gap between image creation and understanding through its unified design.

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GitHub

  1. GitHub - qqlu/Entity: EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation (698 stars)