EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models
Summary: arXiv:2604.11043v1 Announce Type: new
Abstract: Unified multimodal embedding spaces underpin practical applications such as cross-modal retrieval and zero-shot recognition. In many real deployments, however, supervision is available only for a small subset of modality pairs (e.g., image–text), leaving unpaired modality pairs (e.g., audio↔depth, infrared↔audio) weakly connected and thus performing poorly on zero-shot transfer. Addressing this sparse-pairing regime is therefore essential for scaling unified embedding systems to new tasks without curating exhaustive pairwise data.
We propose EmergentBridge, an embedding-level bridging framework that improves performance on these unpaired pairs without requiring exhaustive pairwise supervision. Our key observation is that naively aligning a new modality to a synthesized proxy embedding can introduce gradient interference, degrading the anchor-alignment structure that existing retrieval/classification relies on. EmergentBridge addresses this by:
- Learning a mapping that produces a noisy bridge anchor (a proxy embedding of an already-aligned modality) from an anchor embedding.
- Enforcing proxy alignment only in the subspace orthogonal to the anchor-alignment direction, preserving anchor alignment while strengthening non-anchor connectivity.
Across nine datasets spanning multiple modalities, EmergentBridge consistently outperforms prior binding baselines on zero-shot classification and retrieval, demonstrating strong emergent alignment.
Key Features of EmergentBridge
EmergentBridge introduces several innovative features that significantly enhance the performance of unified multimodal embedding systems:
- No Exhaustive Supervision: The framework operates effectively without the need for extensive pairwise supervision, which is often impractical in real-world applications.
- Gradient Interference Mitigation: By addressing gradient interference, EmergentBridge ensures that the alignment structure remains intact, thus improving the overall performance of the model.
- Robust Proxy Alignment: The approach allows for effective alignment in unpaired modality scenarios, enhancing the model’s ability to generalize across different data types.
Potential Applications
The advancements presented by EmergentBridge have the potential to revolutionize various fields by enabling more efficient and effective cross-modal applications. Some potential applications include:
- Cross-Modal Retrieval: Improved retrieval systems that can understand and interrelate data across different modalities, such as images, text, and audio.
- Zero-Shot Recognition: Enhanced recognition capabilities in scenarios where training data is limited or unavailable, allowing models to recognize unseen classes.
- Multimodal AI Systems: Enabling the development of sophisticated AI systems that can seamlessly integrate and process information from various sources.
Conclusion
EmergentBridge presents a significant advancement in the field of unified multimodal embedding models. By addressing the challenges associated with unpaired modality pairs and improving the performance of zero-shot cross-modal transfer, it opens up new possibilities for practical applications and research advancements. The strong results across multiple datasets highlight the framework’s potential to enhance the capabilities of AI systems in an increasingly multimodal world.