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DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model
arXiv – CS AI|Shibo Hong, Boxian Ai, Jun Kuang, Wei Wang, FengJiao Chen, Zhongyuan Peng, Chenhao Huang, Yixin Cao||2 views
🤖AI Summary
Researchers introduce DLEBench, the first benchmark specifically designed to evaluate instruction-based image editing models' ability to edit small-scale objects that occupy only 1%-10% of image area. Testing on 10 models revealed significant performance gaps in small object editing, highlighting a critical limitation in current AI image editing capabilities.
Key Takeaways
- →DLEBench is the first benchmark dedicated to evaluating small-scale object editing in instruction-based image editing models.
- →The benchmark contains 1889 samples across seven instruction types with target objects occupying only 1%-10% of image area.
- →Testing revealed significant performance gaps in all 10 evaluated image editing models for small object manipulation.
- →The benchmark introduces a dual-mode evaluation framework to address misalignment between AI judges and human assessments.
- →Results highlight the need for specialized benchmarks to advance precise local editing capabilities in AI systems.
#ai-benchmarks#image-editing#computer-vision#machine-learning#ai-evaluation#deep-learning#arxiv#small-object-detection
Read Original →via arXiv – CS AI
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