Researchers have developed a new method called Hide and Seek (HnS) for interpreting black-box vision models. Unlike previous methods, HnS does not require access to model weights or concept-annotated datasets. It works by retrieving concept-aligned images, localizing the concept within those images, masking the localized region, and then measuring the change in the model's output. This approach allows for more versatile concept-level attribution across various vision tasks, including image captioning and segmentation, and has shown improved agreement with ground-truth attributions in validation studies. AI
IMPACT Enables more transparent and interpretable AI systems by providing a method to understand black-box vision models without requiring direct access to their internal workings.
RANK_REASON The cluster contains a research paper detailing a new method for explaining AI models. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- CatalyzeX
- CelebA
- COCO-Stuff
- Connected Papers
- DagsHub
- Gotit.pub
- Hide and Seek
- Hugging Face
- Litmaps
- ScienceCast
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