When Semantics Mislead Vision: Mitigating Large Multimodal Models
  Hallucinations in Scene Text Spotting and Understanding
Large Multimodal Models (LMMs) have achieved impressive progress in visual
perception and reasoning. However, when confronted with visually ambiguous or
non-semantic scene text, they often struggle to accurately spot and understand
the content, frequently generating semantically plausible yet visually
incorrect answers, which we refer to as semantic hallucination. In this work,
we investigate the underlying causes of semantic hallucination and identify a
key finding: Transformer layers in LLM with stronger attention focus on scene
text regions are less prone to producing semantic hallucinations. Thus, we
propose a training-free semantic hallucination mitigation framework comprising
two key components: (1) ZoomText, a coarse-to-fine strategy that identifies
potential text regions without external detectors; and (2) Grounded Layer
Correction, which adaptively leverages the internal representations from layers
less prone to hallucination to guide decoding, correcting hallucinated outputs
for non-semantic samples while preserving the semantics of meaningful ones. To
enable rigorous evaluation, we introduce TextHalu-Bench, a benchmark of over
1,730 samples spanning both semantic and non-semantic cases, with manually
curated question-answer pairs designed to probe model hallucinations. Extensive
experiments demonstrate that our method not only effectively mitigates semantic
hallucination but also achieves strong performance on public benchmarks for
scene text spotting and understanding.
684753683ec10bdd8ab4dd1d
Yan Shu
684753683ec10bdd8ab4dd1e
Hangui Lin
684753683ec10bdd8ab4dd1f
Yexin Liu
684753683ec10bdd8ab4dd20
Yan Zhang
684753683ec10bdd8ab4dd21
Gangyan Zeng
684753683ec10bdd8ab4dd22
Yan Li
684753683ec10bdd8ab4dd23
Yu Zhou
684753683ec10bdd8ab4dd24
Ser-Nam Lim
684753683ec10bdd8ab4dd25
Harry Yang
684753683ec10bdd8ab4dd26
Nicu Sebe
When Semantics Mislead Vision: Mitigating Large Multimodal Models
  Hallucinations in Scene Text Spotting and Understanding
Large Multimodal Models (LMMs) have achieved impressive progress in visual
perception and reasoning. However, when confronted with visually ambiguous or
non-semantic scene text, they often struggle to accurately spot and understand
the content, frequently generating semantically plausible yet visually
incorrect answers, which we refer to as semantic hallucination. In this work,
we investigate the underlying causes of semantic hallucination and identify a
key finding: Transformer layers in LLM with stronger attention focus on scene
text regions are less prone to producing semantic hallucinations. Thus, we
propose a training-free semantic hallucination mitigation framework comprising
two key components: (1) ZoomText, a coarse-to-fine strategy that identifies
potential text regions without external detectors; and (2) Grounded Layer
Correction, which adaptively leverages the internal representations from layers
less prone to hallucination to guide decoding, correcting hallucinated outputs
for non-semantic samples while preserving the semantics of meaningful ones. To
enable rigorous evaluation, we introduce TextHalu-Bench, a benchmark of over
1,730 samples spanning both semantic and non-semantic cases, with manually
curated question-answer pairs designed to probe model hallucinations. Extensive
experiments demonstrate that our method not only effectively mitigates semantic
hallucination but also achieves strong performance on public benchmarks for
scene text spotting and understanding.
A framework using Transformer layers with strong attention and a coarse-to-fine strategy reduces semantic hallucination in LMMs for visual perception and reasoning.
Transformers attention focus semantic hallucination ZoomText Grounded Layer Correction TextHalu-Bench scene text spotting scene text understanding