Multi-Object Hallucination in Vision-Language Models
Large vision language models (LVLMs) often suffer from object hallucination,
producing objects not present in the given images. While current benchmarks for
object hallucination primarily concentrate on the presence of a single object
class rather than individual entities, this work systematically investigates
multi-object hallucination, examining how models misperceive (e.g., invent
nonexistent objects or become distracted) when tasked with focusing on multiple
objects simultaneously. We introduce Recognition-based Object Probing
Evaluation (ROPE), an automated evaluation protocol that considers the
distribution of object classes within a single image during testing and uses
visual referring prompts to eliminate ambiguity. With comprehensive empirical
studies and analysis of potential factors leading to multi-object
hallucination, we found that (1) LVLMs suffer more hallucinations when focusing
on multiple objects compared to a single object. (2) The tested object class
distribution affects hallucination behaviors, indicating that LVLMs may follow
shortcuts and spurious correlations.(3) Hallucinatory behaviors are influenced
by data-specific factors, salience and frequency, and model intrinsic
behaviors. We hope to enable LVLMs to recognize and reason about multiple
objects that often occur in realistic visual scenes, provide insights, and
quantify our progress towards mitigating the issues.
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Xuweiyi Chen
admin_assigned
2024-07-10T11:57:26.951Z
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Xuweiyi Chen
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Xuweiyi
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Ziqiao Ma
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2024-07-18T09:08:15.401Z
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Martin Ziqiao Ma
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marstin
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Xuejun Zhang
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2024-07-10T11:57:47.199Z
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Xuejun Zhang
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yifan-Eva
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Sihan Xu
admin_assigned
2024-07-10T11:58:02.773Z
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Sihan XU
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sihanxu
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Shengyi Qian
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2024-07-10T11:58:08.541Z
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Shengyi Qian
user
shengyi-qian
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Jianing Yang
claimed_verified
2024-07-09T21:09:06.952Z
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Jed Yang
user
jedyang97
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David F. Fouhey
668d5680f4ab25acfedd9bf1
Joyce Chai
Multi-Object Hallucination in Vision-Language Models
Large vision language models (LVLMs) often suffer from object hallucination,
producing objects not present in the given images. While current benchmarks for
object hallucination primarily concentrate on the presence of a single object
class rather than individual entities, this work systematically investigates
multi-object hallucination, examining how models misperceive (e.g., invent
nonexistent objects or become distracted) when tasked with focusing on multiple
objects simultaneously. We introduce Recognition-based Object Probing
Evaluation (ROPE), an automated evaluation protocol that considers the
distribution of object classes within a single image during testing and uses
visual referring prompts to eliminate ambiguity. With comprehensive empirical
studies and analysis of potential factors leading to multi-object
hallucination, we found that (1) LVLMs suffer more hallucinations when focusing
on multiple objects compared to a single object. (2) The tested object class
distribution affects hallucination behaviors, indicating that LVLMs may follow
shortcuts and spurious correlations.(3) Hallucinatory behaviors are influenced
by data-specific factors, salience and frequency, and model intrinsic
behaviors. We hope to enable LVLMs to recognize and reason about multiple
objects that often occur in realistic visual scenes, provide insights, and
quantify our progress towards mitigating the issues.
ROPE, an automated evaluation protocol, examines multi-object hallucination in LVLMs, identifying factors affecting hallucinations and aiming to improve performance on realistic scenes.
Large vision language models LVLMs object hallucination Recognition-based Object Probing Evaluation ROPE visual referring prompts multi-object hallucination shortcuts spurious correlations