Refined Alignment Techniques for CLIP–LLM Integration

Develop refined and effective cross-modal alignment techniques for integrating the pre-trained CLIP image encoder with large language model–based text embedders, so that alignment improves without degrading the generalization ability that is often compromised by direct contrastive alignment strategies.

Background

Recent approaches such as FLAME and LLM2CLIP replace CLIP’s original text encoder with LLM-based embedders to handle longer, multilingual, and more complex text inputs. However, these methods typically rely on direct contrastive alignment between the CLIP image encoder and the LLM-derived text embeddings, which the authors observe can lead to degraded generalization due to misaligned representation spaces and disregard for the original CLIP alignment knowledge.

Within the related work discussion on using LLMs for representation learning, the authors explicitly note that despite promising results, the current alignment strategies remain overly coarse. They characterize the development of more refined and effective alignment methods as a critical open research challenge, motivating the proposed progressive alignment framework (ProCLIP) while acknowledging that broader methodological advances are still needed.