Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models

• URL: http://arxiv.org/abs/2310.05338v2
• Date: Tue, 13 Aug 2024 05:48:31 GMT
• Title: Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
• Authors: Holy Lovenia, Wenliang Dai, Samuel Cahyawijaya, Ziwei Ji, Pascale Fung,
• Abstract summary: NOPE (Negative Object Presence Evaluation) is a novel benchmark designed to assess object hallucination in vision-language (VL) models. We extensively investigate the performance of 10 state-of-the-art VL models in discerning the non-existence of objects in visual questions.
• Score: 67.8024390595066
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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