Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models
For practitioners deploying VLMs in text-only scenarios, LIM offers a practical method to improve reliability without retraining the backbone.
Vision-language models suffer from accuracy drops and miscalibration when deployed on text-only inputs. The proposed Latent Imagination Module (LIM) improves accuracy and reduces calibration error by predicting imagined latent embeddings from text, without generating images.
Vision-language models (VLMs) are often deployed on text-only inputs, although they are trained with images. We find that removing the vision modality causes large drops in accuracy and severe miscalibration, and the model does not behave like its original language backbone under text-only prompting. This failure is not explained only by missing semantic information. Even when text descriptions preserve key content, confidence becomes unreliable, while adding a visual signal through generated images partially restores accuracy and calibration. We propose the Latent Imagination Module (LIM), a lightweight cross-attention module that predicts imagined latent embeddings from textual input and feeds them into a frozen VLM backbone without pixel-level image synthesis. Across text-only benchmarks, unseen tasks, and missing-image scenarios, LIM improves accuracy and reduces calibration error. These results suggest that latent modality completion is a practical approach for reliable VLM inference under missing-modality.