arXiv Computer Vision
Do Words Matter? What Text Contributes to Few and Full-Shot Vision-Language Anomaly Detection
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When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models
arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.
arXiv AI
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One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models
The paper investigates how the modality gap— the separation between image and text representations in contrastive vision‑language models—affects different downstream tasks. By showing that a single dominant direction accounts for most of the image‑text mean separation, the authors explain why reducing or removing this gap can improve zero‑shot classification, degrade retrieval, or restore performance depending on the task. The study provides a geometric framework that clarifies when and why gap interventions should be applied in vision‑language systems.
arXiv Computer Vision
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Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
The paper introduces Visual Retrieval Heads (VRHs), a small fraction of attention heads in vision‑language models that are causally responsible for grounding text descriptions to image regions. By recasting head‑scoring methods and evaluating across eleven VLMs and five benchmarks, the authors show that masking the top 20 VRHs can drop grounding accuracy by up to 80 percentage points, while random masking has little effect. VRHs generalize across various visual reference tasks, preserve output format while corrupting localization, and transfer causally across models sharing an LLM backbone.
arXiv Computer Vision
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DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection
arXiv:2608.23723v1 Announce Type: new Abstract: Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual f...
arXiv Computer Vision
Aug 28
When Semantics Saturate or Emerge: Adaptation-Conditional Semantic Utility in Source-Free Cross-Domain Few-Shot Learning
The paper investigates whether language prompts selected by zero‑shot accuracy remain effective after visual adaptation in source‑free cross‑domain few‑shot learning. Using a paired protocol, the authors compare generic class‑name templates with detailed class descriptions before and after Low‑Rank Adaptation (LoRA) on datasets such as EuroSAT, CropDisease, ISIC, and ChestX. They identify two regimes: semantic saturation, where detailed prompts are already useful before adaptation, and semantic emergence, where detailed prompts become more useful only after visual representation updates, driven by changes in prediction patterns.
Hugging Face Trending Papers
Aug 27
Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
The paper demonstrates that vision‑language models (VLMs) possess a small set of attention heads, called Visual Retrieval Heads (VRHs), that are causally responsible for linking text prompts to specific image regions. By adapting head‑scoring techniques from language models, the authors identify VRHs as the heads whose attention from output prediction tokens, summed over the ground‑truth referent region, most reliably indicates causal grounding. Experiments across eleven VLMs and five referring‑expression benchmarks show that masking the top 20 VRHs can drop grounding accuracy by up to 80 percentage points, while random masking has little effect, and that VRHs generalize across diverse visual tasks and transfer across models sharing an LLM backbone.