ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval (2502.15682v2)

Abstract: The objective in this paper is to improve the performance of text-to-image retrieval. To this end, we introduce a new framework that can boost the performance of large-scale pre-trained vision-LLMs, so that they can be used for text-to-image re-ranking. The approach, Enhanced Language-Image Pre-training (ELIP), uses the text query, via a simple MLP mapping network, to predict a set of visual prompts to condition the ViT image encoding. ELIP can easily be applied to the commonly used CLIP, SigLIP and BLIP-2 networks. To train the architecture with limited computing resources, we develop a 'student friendly' best practice, involving global hard sample mining, and curation of a large-scale dataset. On the evaluation side, we set up two new out-of-distribution (OOD) benchmarks, Occluded COCO and ImageNet-R, to assess the zero-shot generalisation of the models to different domains. The results demonstrate that ELIP significantly boosts CLIP/SigLIP/SigLIP-2 text-to-image retrieval performance and outperforms BLIP-2 on several benchmarks, as well as providing an easy means to adapt to OOD datasets.

Enhanced Visual-Language Foundation Models for Image Retrieval: A Review of ELIP

The advent of large-scale pre-trained vision-LLMs such as CLIP, SigLIP, and BLIP-2 has significantly advanced the domain of text-to-image retrieval. However, the crucial phase of re-ranking, which enhances retrieval accuracy by refining initial results, still requires optimization. The paper "ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval" introduces a novel framework aimed at leveraging these foundational models to improve re-ranking performance efficiently and effectively. Here, we will examine the structure, methodology, and impact of ELIP on this challenging task.

ELIP Architecture and Methodology

The core of ELIP, or Enhanced Language-Image Pre-training, integrates a text-guided visual prompt mechanism designed to operate within the existing architecture of foundation models like CLIP and BLIP-2. The central innovation lies in a lightweight MLP mapping network, which translates text queries into visual prompt vectors. These vectors are then appended to the visual feature space, guiding the image encoder to refine its representation by emphasizing image-text alignment characteristics pertinent to the query.

1. Text-Guided Visual Prompting: The novel aspect of ELIP is its ability to condition the image encoding process on the text query via visual prompts. These are generated by mapping the text embeddings into visual space, effectively transforming the textual context into a form that the visual encoder can utilize. This step is critical in aligning the model’s attention to text-specified image details.
2. Efficient Adaptability: ELIP is designed to minimally alter the computational demands by adding only a few trainable parameters. Moreover, the visual prompt approach can be appended to pre-existing models, facilitating seamless upgrades to model capabilities without extensive retraining.
3. Data Curation Strategies: Recognizing the resource-intensive nature of training large models, ELIP incorporates a unique data curation process that emphasizes hard sample mining. Batches are curated based on sample difficulty, ensuring informative and challenging data drives the training process. This approach permits effective learning with smaller batch sizes and reduced computational overhead.
4. Out-of-Distribution Evaluation: Two benchmarks, Occluded COCO and ImageNet-R, are established to evaluate ELIP's zero-shot generalization. These benchmarks test the model's proficiency in recognizing occluded objects and domains deviating from training data, respectively, providing a robust evaluation framework that extends beyond conventional settings.

Performance and Implications

Empirical results demonstrate ELIP's capability to enhance retrieval performance significantly. On conventional benchmarks like COCO and Flickr30k, implementing ELIP notably improves recall metrics compared to base models like CLIP and BLIP-2. For example, ELIP-C and ELIP-S variants outperform their respective baselines across Recall@1, Recall@5, and Recall@10, validating its re-ranking prowess.

The testing on Occluded COCO and ImageNet-R underscores ELIP's adaptability, showing marked improvements in mAP scores over baselines, thereby corroborating its robustness in out-of-distribution scenarios. This capability suggests potential applications in automated systems requiring dynamic image understanding, such as autonomous vehicles encountering unforeseen visual contexts or advanced search engines.

Future Directions

The methodologies and results presented invite several avenues for exploration and extension. Continuous refinement of the visual prompt component could lead to even more efficient models, particularly if combined with advances in language representation techniques. Moreover, extending ELIP’s adaptive strategies to domains like video-text models or 3D imagery could broaden the scope of intelligent retrieval systems.

Moreover, while ELIP presents significant enhancements, further work is necessary to address scalability across even larger datasets and more diverse domains. Cross-disciplinary collaborations could surface more intricate patterns within multimodal datasets, potentially influencing ELIP's architecture and vision-LLMs in general.

Conclusion

The "ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval" paper integrates text-contextualized visual prompts into existing foundational models to achieve superior image retrieval performance. Its lightweight architecture, informed data curation strategies, and robust OOD performance combine to create a powerful tool in the domain of image retrieval. ELIP stands at the forefront of enhancing vision-language interactions and sets a solid foundation for future advancements in this rapidly evolving field.