DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model
arXiv cs.CV / 3/2/2026
Ideas & Deep AnalysisModels & Research
Key Points
- DLEBench is the first dedicated benchmark designed to evaluate instruction-based image editing models' ability to edit small-scale objects precisely.
- The benchmark includes 1889 challenging samples covering complex scenarios like partial occlusion and multi-object editing with targets occupying 1%-10% of image area.
- A refined evaluation protocol with criteria for Instruction Following and Visual Consistency reduces subjectivity and includes dual evaluation modes to align AI and human judgments.
- Testing 10 instruction-based image editing models revealed significant performance gaps in small object editing, indicating a need for specialized benchmarks and model improvements.
- This work highlights the importance of precise local editing and detail refinement for practical and high-quality image editing applications.
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