Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
NOPE evaluates object hallucination in vision-language models, finding all models score below 10% accuracy on NegP datasets.
Key Findings
Methodology
NOPE assesses object hallucination through visual question answering, using large language models to generate 29.5k high-quality NegP data. It investigates the performance of 10 state-of-the-art vision-language models in recognizing the non-existence of objects in visual questions.
Key Results
- All models scored below 10% accuracy on NegP datasets, indicating no model is immune to object hallucination.
- Standard performance evaluations on 9 other VQA datasets show models' performance on typical visual questions.
- Lexically diverse visual questions, broad question types, and scene-relevant objects increase the risk of object hallucination.
Significance
This study provides a general evaluation standard for object hallucination in vision-language models, aiding in understanding and mitigating this issue. NOPE helps researchers identify and address object hallucination in models.
Technical Contribution
NOPE offers a new method for evaluating object hallucination, with greater generality and scalability compared to existing methods. It uses large-scale language models to generate high-quality NegP data, providing a new avenue for studying object hallucination.
Novelty
NOPE is the first benchmark specifically for evaluating object hallucination in vision-language models, using a unique NegP data generation method to fill gaps in existing evaluation approaches.
Limitations
- Models perform poorly on lexically diverse questions, prone to object hallucination.
- Existing models have insufficient understanding of scene-relevant objects, leading to hallucinations.
Future Work
Future work could explore improving models' performance on high lexical diversity and scene-relevant objects, developing more robust vision-language models.
AI Executive Summary
Object hallucination poses a significant challenge for vision-language models, often leading to nonsensical or unfaithful responses. Existing solutions lack a general evaluation standard, making it difficult to effectively identify and mitigate this issue. In response, researchers have introduced NOPE to evaluate object hallucination through visual question answering. NOPE uses large language models to generate high-quality NegP data, assessing the performance of 10 state-of-the-art vision-language models in recognizing the non-existence of objects. Experimental results show that all models score below 10% accuracy on NegP datasets, indicating no model is immune to object hallucination. This study provides a general evaluation standard for object hallucination in vision-language models, aiding in understanding and mitigating this issue. Future work could explore improving models' performance on high lexical diversity and scene-relevant objects, developing more robust vision-language models.
Deep Analysis
Background
In recent years, the field of vision-language research has seen a proliferation of methods and models aimed at bridging the performance gap between human and model capabilities. Despite these efforts, vision-language models continue to grapple with the persistent issue of object hallucination, where generated responses unfaithfully contain objects non-existent in the input images.
Core Problem
Object hallucination is a major cause of unfaithful answers in vision-language models. Existing evaluation methods face challenges in generalization and scalability, making it difficult to effectively assess object hallucination.
Innovation
NOPE evaluates object hallucination through visual question answering, using large language models to generate high-quality NegP data. Compared to existing methods, NOPE offers greater generality and scalability.
Methodology
- �� Use large language models to generate 29.5k NegP data
- �� Evaluate 10 vision-language models in recognizing non-existent objects
- �� Analyze the impact of lexical diversity, question types, and scene-relevant objects on object hallucination
Experiments
The experimental design includes evaluating 10 vision-language models on NegP datasets, and conducting standard performance evaluations on 9 other VQA datasets. By analyzing the impact of lexical diversity and scene-relevant objects on model performance, the study reveals risk factors for object hallucination.
Results
All models scored below 10% accuracy on NegP datasets, indicating no model is immune to object hallucination. Lexically diverse visual questions, broad question types, and scene-relevant objects increase the risk of object hallucination.
Applications
NOPE can be used to evaluate object hallucination in vision-language models, helping researchers identify and address object hallucination issues in models.
Limitations & Outlook
Models perform poorly on lexically diverse questions, prone to object hallucination. Existing models have insufficient understanding of scene-relevant objects, leading to hallucinations.
Plain Language Accessible to non-experts
Imagine you're looking at a painting with a bench, but no one is sitting there. Vision-language models sometimes mistakenly think there's a person sitting, which is object hallucination. NOPE is like a detective, specifically finding these errors. It checks if models can correctly identify objects in the painting by asking questions like 'Is there anyone here?' Through this method, NOPE helps us identify which models are prone to mistakes and figure out how to improve them.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of pictures. Your task is to find things that aren't in the pictures, like 'Is there a cat here?' Sometimes, the game gives you wrong answers, saying there's a cat when there isn't. That's object hallucination. NOPE is like a super smart assistant, helping you find these mistakes. It checks if the game's answers are correct by asking clever questions. This way, we can know which games are prone to mistakes and figure out how to improve them.
Glossary
Vision-Language Models
Models that process both visual and language information.
Used in the study to evaluate object hallucination.
Object Hallucination
Responses generated by models containing non-existent objects.
NOPE is used to identify and evaluate object hallucination.
NegP Data
Negative pronoun data indicating non-existent objects.
Used in NOPE to evaluate object hallucination.
NOPE
Benchmark for evaluating object hallucination in vision-language models.
Evaluates object hallucination through visual question answering.
Visual Question Answering
Task of answering questions based on visual information.
Used in NOPE to evaluate object hallucination.
Open Questions Unanswered questions from this research
- 1 How to improve model performance on lexically diverse questions?
- 2 How to reduce hallucination on scene-relevant objects?
Applications
Immediate Applications
Model Evaluation
NOPE can be used to evaluate object hallucination in vision-language models, helping researchers identify and address object hallucination issues in models.
Long-term Vision
Model Improvement
Using NOPE's evaluation results, develop more robust vision-language models to reduce object hallucination and improve model accuracy.
Abstract
Object hallucination poses a significant challenge in vision-language (VL) models, often leading to the generation of nonsensical or unfaithful responses with non-existent objects. However, the absence of a general measurement for evaluating object hallucination in VL models has hindered our understanding and ability to mitigate this issue. In this work, we present NOPE (Negative Object Presence Evaluation), a novel benchmark designed to assess object hallucination in VL models through visual question answering (VQA). We propose a cost-effective and scalable approach utilizing large language models to generate 29.5k synthetic negative pronoun (NegP) data of high quality for NOPE. We extensively investigate the performance of 10 state-of-the-art VL models in discerning the non-existence of objects in visual questions, where the ground truth answers are denoted as NegP (e.g., "none"). Additionally, we evaluate their standard performance on visual questions on 9 other VQA datasets. Through our experiments, we demonstrate that no VL model is immune to the vulnerability of object hallucination, as all models achieve accuracy below 10\% on NegP. Furthermore, we uncover that lexically diverse visual questions, question types with large scopes, and scene-relevant objects capitalize the risk of object hallucination in VL models.