Seeing Is Not Addressing: Auditing Linguistic Access to Frozen Visual Geometry

arXiv:2609.37230 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: 27 pages, 10 figures. Code available at https://github.com/LABA-SNU/seeing-is-not-addressing

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Grounding — 122× in this paper · explained in 3 episodes
• Calibration — 59× in this paper · explained in 7 episodes
• Alignment — 34× in this paper · explained in 1 episode
• Visual Grounding — 23× in this paper · explained in 2 episodes
• Addressability — 13× in this paper · explained in 1 episode
• Embeddings — 9× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• International Conference on Computer Vision — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models
• Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP
• Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models
• Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrieval
• Interpreting the linear structure of vision-language model embedding spaces
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• FG-CLIP: Fine-Grained Visual and Textual Alignment
• ABE-CLIP: Training-Free Attribute Binding Enhancement for Compositional Image-Text Matching
• Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
• VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations

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