Binary Verification for Zero-Shot Vision

📰 ArXiv cs.AI

Binary verification workflow for zero-shot vision using off-the-shelf vision-language models (VLMs)

advanced Published 30 Mar 2026

Action Steps

Quantize the open-ended query into a multiple-choice question with a small list of candidates
Binarize the query by asking one True/False question per candidate
Resolve deterministically by selecting the candidate if exactly one is True, otherwise revert to a multiple-choice question

Who Needs to Know This

Computer vision engineers and researchers can benefit from this workflow to improve the accuracy of zero-shot vision tasks, while machine learning engineers can apply this method to develop more efficient vision models

Key Insight

💡 Binary verification can improve the accuracy of zero-shot vision tasks without requiring additional training

Key Takeaways

Binary verification workflow for zero-shot vision using off-the-shelf vision-language models (VLMs)

Full Article

Title: Binary Verification for Zero-Shot Vision

Abstract:
arXiv:2511.10983v2 Announce Type: replace-cross Abstract: We propose a training-free, binary verification workflow for zero-shot vision with off-the-shelf VLMs. It comprises two steps: (i) quantization, which turns the open-ended query into a multiple-choice question (MCQ) with a small, explicit list of unambiguous candidates; and (ii) binarization, which asks one True/False question per candidate and resolves deterministically: if exactly one is True, select it; otherwise, revert to an MCQ over

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