arxiv_ml 92% Match Research Paper Computer vision researchers,ML engineers,Developers of AI-powered image analysis tools 2 weeks ago

gen2seg: Generative Models Enable Generalizable Instance Segmentation

computer-vision › scene-understanding

📄 Abstract

Abstract: By pretraining to synthesize coherent images from perturbed inputs, generative models inherently learn to understand object boundaries and scene compositions. How can we repurpose these generative representations for general-purpose perceptual organization? We finetune Stable Diffusion and MAE (encoder+decoder) for category-agnostic instance segmentation using our instance coloring loss exclusively on a narrow set of object types (indoor furnishings and cars). Surprisingly, our models exhibit strong zero-shot generalization, accurately segmenting objects of types and styles unseen in finetuning (and in many cases, MAE's ImageNet-1K pretraining too). Our best-performing models closely approach the heavily supervised SAM when evaluated on unseen object types and styles, and outperform it when segmenting fine structures and ambiguous boundaries. In contrast, existing promptable segmentation architectures or discriminatively pretrained models fail to generalize. This suggests that generative models learn an inherent grouping mechanism that transfers across categories and domains, even without internet-scale pretraining. Code, pretrained models, and demos are available on our website.

Authors (2)

Om Khangaonkar

Hamed Pirsiavash

Submitted

May 21, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

Demonstrates that generative models like Stable Diffusion and MAE, when fine-tuned with an instance coloring loss, can achieve strong zero-shot generalization for instance segmentation, even for object types unseen during fine-tuning. This approach significantly outperforms existing promptable segmentation models and approaches supervised methods like SAM on unseen categories.

Business Value

Enables more flexible and adaptable vision systems that can segment novel objects without extensive retraining, reducing development costs and time for applications requiring object recognition.

Paper Metadata

Innovation Type

Methodological and Application

Deployment Feasibility

Moderate. Requires fine-tuning pre-trained generative models, which can be computationally intensive, but the resulting models are efficient for inference.

Limitations Addressed

Poor generalization of instance segmentation models to unseen object categories and styles; reliance on large, labeled datasets for supervised methods.

Performance Gains

Achieves strong zero-shot generalization, approaching supervised methods like SAM on unseen object types and styles, and outperforming other promptable segmentation architectures.

Technical Tags

instance segmentationgenerative modelsStable DiffusionMAEzero-shot generalizationcategory-agnostic segmentationobject boundariesscene compositioninstance coloring lossSAM

Research Topics

Computer VisionGenerative ModelsInstance SegmentationZero-Shot LearningRepresentation Learning

Methods & Architectures

Fine-tuning Stable DiffusionFine-tuning MAE (encoder+decoder)Instance coloring lossCategory-agnostic segmentation Stable DiffusionMasked Autoencoders (MAE)

Applications & Tasks

Computer Vision Image Analysis Robotics Perception Generalizable instance segmentationCategory-agnostic segmentationLeveraging generative model representations Instance segmentation of objects, especially unseen types and styles

Datasets & Benchmarks

Datasets

ImageNet-1K

Benchmarks

Performance comparable to SAM on unseen object types and styles

Segmentation accuracyZero-shot generalization performance

Related Fields

Computer VisionGenerative ModelsDeep LearningRepresentation LearningZero-Shot Learning

Keywords

instance segmentationgenerative modelsdiffusion modelsMAEzero-shot learninggeneralizationobject detectioncomputer visionimage segmentationSAM

Academic Context

#Computer Vision#Generative Models#Instance Segmentation#Zero-Shot Learning#Representation Learning

Technology Stack

Frameworks & Libraries

Stable DiffusionMAE

Commercial Potential

Potential Products

General-purpose instance segmentation APIsAI tools for image annotation and analysis

Target Industries

E-commerceAutonomous DrivingRoboticsContent ModerationMedical Imaging

Use Case Examples

Segmenting diverse products in online retail imagesIdentifying objects for robotic manipulation in unstructured environmentsAutomated image tagging and cataloging

Competitive Edge

Leverages the inherent understanding of structure and boundaries learned by generative models, offering superior zero-shot generalization compared to discriminatively trained models or existing promptable segmentation methods.

Market Opportunity

Large market for image analysis and segmentation tools.

Revenue Models

Licensing of segmentation modelsintegration into SaaS platforms for image analysis.

Resource Requirements

Compute Needs

High for fine-tuning, moderate for inference.

Data Requirements

Requires datasets for fine-tuning (e.g., indoor furnishings, cars) and evaluation datasets for zero-shot generalization.

Deployment Constraints

Fine-tuning requires significant computational resources; performance on highly complex or abstract scenes might still be limited.

Scalability

Inference is generally efficient, but fine-tuning scalability depends on model size and dataset.

Regulatory Considerations

Bias in segmentation results if training data is not diverse; ethical use in surveillance or content moderation.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years for integration into commercial vision systems.

Patent Potential

Moderate, for novel fine-tuning techniques or model architectures.

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