NeurIPS Paper: Is the Modality Gap in Multimodal Models a Bug or Feature?
gabriberton · x · 2026-08-19
- Paper Context: The NeurIPS 2022 paper "Mind the Gap" systematically analyzes the "modality gap," where different modalities (e.g., images and text) remain separated in the shared representation space of models like CLIP.
- Causes: The gap is caused by a combination of model initialization (representations restricted to a narrow cone) and contrastive learning optimization, which maintains separation influenced by the temperature parameter.
- Impact: Experiments show that varying the modality gap distance significantly affects downstream zero-shot classification performance and fairness. Closing the gap generally improves classification.
- Open Question: Could the gap be a feature rather than a bug? Perhaps one of the 1000 dimensions should be used to preserve the modality gap for specific tasks.
Related event: Modality Gap in VLMs: Flaw or Feature?(7 posts)→
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