arxiv_cv 95% Match Research Paper AI Researchers,Machine Learning Engineers,Generative Model Developers,Computer Vision Scientists 1 month ago

Visual Self-Refinement for Autoregressive Models

generative-ai › autoregressive

📄 Abstract

Abstract: Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. This work proposes a plug-and-play refinement module to enhance the complex spatial correspondence modeling within the generated visual sequence. This module operates as a post-pretraining step to jointly refine all generated tokens of autoregressive model, enhancing vision-language modeling under a shared sequential prediction framework. By leveraging global context and relationship across the tokens, our method mitigates the error accumulation issue within the sequential generation. Experiments demonstrate that the proposed method improves the generation quality, enhancing the model's ability to produce semantically consistent results.

Key Contributions

This paper proposes a plug-and-play Visual Self-Refinement module for autoregressive models, particularly in vision-language tasks. This module operates as a post-pretraining step to jointly refine all generated tokens, enhancing spatial correspondence modeling and mitigating error accumulation by leveraging global context across the sequence.

Business Value

Leads to more coherent and accurate generated images and videos from autoregressive models, improving the quality of AI-generated content for creative and practical applications.

Paper Metadata

Innovation Type

Algorithmic/Module Addition

Deployment Feasibility

High, as it's a plug-and-play module that can be added to existing autoregressive models after their initial pretraining.

Limitations Addressed

The inherent conflict between the spatial nature of visual signals and the sequential dependencies of next-token prediction in autoregressive models, leading to suboptimal generation quality and error accumulation.

Performance Gains

Improves generation quality and enhances the model's ability to produce semantically consistent results.

Technical Tags

Autoregressive ModelsVision-Language ModelsVisual Self-RefinementSequential ModelingSpatial CorrespondenceToken GenerationError AccumulationPlug-and-Play ModuleGlobal Context

Research Topics

Generative ModelsAutoregressive ModelsVision-Language ModelingSequence GenerationDeep Learning

Methods & Architectures

Visual Self-Refinement ModulePost-Pretraining RefinementJoint Token RefinementLeveraging Global Context Autoregressive Models

Applications & Tasks

Image Generation Video Generation Text-to-Image Synthesis Vision-Language Tasks Suboptimal Results in Vision-Language Autoregressive ModelsConflict between Spatial Visual Signals and Sequential DependenciesError Accumulation in GenerationImproving Spatial Correspondence Modeling Image GenerationVideo GenerationText-to-Image SynthesisVision-Language Generation

Related Fields

Generative AIDeep LearningComputer VisionNatural Language ProcessingSequence Modeling

Keywords

autoregressive modelsvision-languageself-refinementsequential generationspatial correspondencetoken refinementerror accumulationplug-and-playgenerative AIdeep learning

Academic Context

#Generative Models#Autoregressive Models#Vision-Language Modeling#Sequence Generation#Deep Learning

Commercial Potential

Potential Products

Improved text-to-image and text-to-video generation toolsMore coherent AI-generated creative content

Target Industries

Media and EntertainmentAdvertisingGamingDesign

Use Case Examples

Generating more realistic and contextually accurate images from text descriptionsCreating videos with better temporal consistencyEnhancing the output of large language models for creative tasks

Competitive Edge

Addresses a fundamental challenge in autoregressive generation for vision-language tasks by introducing a post-hoc refinement strategy that improves spatial coherence and reduces errors.

Market Opportunity

Growing market for generative AI and content creation tools.

Revenue Models

Integration into generative AI platformslicensing of the refinement module.

Resource Requirements

Compute Needs

Requires additional compute for the refinement step, but potentially less than end-to-end retraining.

Data Requirements

Requires datasets suitable for training autoregressive vision-language models.

Deployment Constraints

Adds an extra step to the generation pipeline.

Scalability

Scalability depends on the efficiency of the refinement module and the length of the generated sequence.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years

Patent Potential

Moderate, related to novel refinement techniques for sequence generation.

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