Meta-CoT: Enhancing Granularity and Generalization in Image Editing

About this episode

This episode discusses the Meta-CoT paradigm for enhancing image editing through improved understanding granularity and generalization.

🤗 Upvotes: 24 | cs.CV, cs.AI, cs.LG, cs.MM Authors: Shiyi Zhang, Yiji Cheng, Tiankai Hang, Zijin Yin, Runze He, Yu Xu, Wenxun Dai, Yunlong Lin, Chunyu Wang, Qinglin Lu, Yansong Tang Title: Meta-CoT: Enhancing Granularity and Generalization in Image Editing Arxiv: http://arxiv.org/abs/2604.24625v1 Abstract: Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To address this, we propose Meta-CoT, a paradigm that performs a two-level decomposition of any single-image editing operation with two key properties: (1) Decomposability. We observe that any editing intention can be represented as a triplet - (task, target, required understanding ability). Inspired by this, Meta-CoT decomposes both the editing task and the target, generating task-specific CoT and traversing editing operations on all targets. This decomposition enhances the model's understanding granularity of editing operations…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• image editing
• multi-modal understanding
• Chain-of-Thought
• generalization
• decomposition
• training strategy

Keywords

• image editing
• Meta-CoT
• Chain-of-Thought
• multi-modal models
• training strategy
• decomposition
• generalization

Mentioned in this episode

Organizations: Meta-CoT, Arxiv

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