DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
📰 ArXiv cs.AI
Learn to diagnose object hallucination in vision-language models using DO-Bench, a new benchmark that isolates the influence of contextual textual priors
Action Steps
- Run DO-Bench on your vision-language model to diagnose object hallucination
- Configure the benchmark to isolate the influence of contextual textual priors
- Test your model's performance on binary object existence verification tasks
- Analyze the results to identify perceptual limitations versus contextual prior influences
- Apply the insights from DO-Bench to improve your model's reliability and accuracy
Who Needs to Know This
Computer vision engineers and researchers working on vision-language models can use DO-Bench to identify and address object hallucination issues, improving model reliability
Key Insight
💡 DO-Bench helps disentangle errors in vision-language models, distinguishing between perceptual limitations and contextual textual prior influences
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🚨 Diagnose object hallucination in vision-language models with DO-Bench! 🚨
Key Takeaways
Learn to diagnose object hallucination in vision-language models using DO-Bench, a new benchmark that isolates the influence of contextual textual priors
Full Article
Title: DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
Abstract:
arXiv:2604.22822v1 Announce Type: cross Abstract: Object level hallucination remains a central reliability challenge for vision language models (VLMs), particularly in binary object existence verification. Existing benchmarks emphasize aggregate accuracy but rarely disentangle whether errors stem from perceptual limitations or from the influence of contextual textual priors, leaving underlying failure mechanisms ambiguous. We introduce DO-Bench, a controlled diagnostic benchmark that isolates th
Abstract:
arXiv:2604.22822v1 Announce Type: cross Abstract: Object level hallucination remains a central reliability challenge for vision language models (VLMs), particularly in binary object existence verification. Existing benchmarks emphasize aggregate accuracy but rarely disentangle whether errors stem from perceptual limitations or from the influence of contextual textual priors, leaving underlying failure mechanisms ambiguous. We introduce DO-Bench, a controlled diagnostic benchmark that isolates th
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