DASH: Detection and Assessment of Systematic Hallucinations of VLMs
Vision-language models (VLMs) are prone to object hallucinations, where they
erroneously indicate the presenceof certain objects in an image. Existing
benchmarks quantify hallucinations using relatively small, labeled datasets.
However, this approach is i) insufficient to assess hallucinations that arise
in open-world settings, where VLMs are widely used, and ii) inadequate for
detecting systematic errors in VLMs. We propose DASH (Detection and Assessment
of Systematic Hallucinations), an automatic, large-scale pipeline designed to
identify systematic hallucinations of VLMs on real-world images in an
open-world setting. A key component is DASH-OPT for image-based retrieval,
where we optimize over the ''natural image manifold'' to generate images that
mislead the VLM. The output of DASH consists of clusters of real and
semantically similar images for which the VLM hallucinates an object. We apply
DASH to PaliGemma and two LLaVA-NeXT models across 380 object classes and, in
total, find more than 19k clusters with 950k images. We study the transfer of
the identified systematic hallucinations to other VLMs and show that
fine-tuning PaliGemma with the model-specific images obtained with DASH
mitigates object hallucinations. Code and data are available at
https://YanNeu.github.io/DASH.
67ecf726f87008271b1d07d5
Maximilian Augustin
admin_assigned
2025-04-03T14:05:03.470Z
67d19d9a3e04b5840ab8684f
/avatars/58b2e28c7360b546141a3cd91a392eed.svg
Maximilian Augustin
user
M4ximal
67ecf726f87008271b1d07d6
Yannic Neuhaus
claimed_verified
2025-04-02T08:51:14.837Z
650971dbce83a0c12a851000
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/ec7H2cZpaZz07W02FT5U9.png
Yannic Neuhaus
user
YanNeu
67ecf726f87008271b1d07d7
Matthias Hein
DASH: Detection and Assessment of Systematic Hallucinations of VLMs
Vision-language models (VLMs) are prone to object hallucinations, where they
erroneously indicate the presenceof certain objects in an image. Existing
benchmarks quantify hallucinations using relatively small, labeled datasets.
However, this approach is i) insufficient to assess hallucinations that arise
in open-world settings, where VLMs are widely used, and ii) inadequate for
detecting systematic errors in VLMs. We propose DASH (Detection and Assessment
of Systematic Hallucinations), an automatic, large-scale pipeline designed to
identify systematic hallucinations of VLMs on real-world images in an
open-world setting. A key component is DASH-OPT for image-based retrieval,
where we optimize over the ''natural image manifold'' to generate images that
mislead the VLM. The output of DASH consists of clusters of real and
semantically similar images for which the VLM hallucinates an object. We apply
DASH to PaliGemma and two LLaVA-NeXT models across 380 object classes and, in
total, find more than 19k clusters with 950k images. We study the transfer of
the identified systematic hallucinations to other VLMs and show that
fine-tuning PaliGemma with the model-specific images obtained with DASH
mitigates object hallucinations. Code and data are available at
https://YanNeu.github.io/DASH.
DASH, an automatic pipeline, identifies and assesses systematic hallucinations in vision-language models across a large dataset, improving model accuracy through fine-tuning with misleading images generated from the natural image manifold.
Detection and Assessment of Systematic Hallucinations DASH DASH-OPT natural image manifold vision-language models object hallucinations fine-tuning