- The paper introduces a novel two-stage framework that fine-tunes CLIP-based models to enhance composed image retrieval performance.
- It employs a Combiner network to effectively merge multimodal features, achieving significant Recall gains on challenging datasets like FashionIQ and CIRR.
- The approach improves the additivity properties of embedding spaces, setting a new benchmark for future research in multimodal retrieval.
An Analysis of "Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features"
This paper presents a comprehensive methodology aimed at enhancing the task of composed image retrieval by leveraging the capabilities of CLIP-based vision-LLMs. The authors propose a two-stage framework that incorporates a novel task-oriented fine-tuning regimen coupled with a strategically designed Combiner network to improve the fusion of multimodal features.
In the field of multimedia retrieval, composed image retrieval is a distinctive task that integrates visual and textual modalities to refine search outcomes. The methodology outlined in this paper capitalizes on the high efficiency of vision and language pre-trained models (VLPs), notably the CLIP model, which utilizes a unified embedding space for visual and textual data. The anchoring hypothesis is that it is beneficial to move from such a combined embedding space toward image and text spaces that exhibit strong additivity properties. Fine-tuning plays a pivotal role in this transformation, enabling the image and text encoders to align more closely with the retrieval objectives, thus maximizing the cohesive effect of the combined features. This approach differs from previous methodologies that either did not prioritize maintaining separate embedding spaces or relied strictly on the pre-existing structures of VLPs without additional task-oriented adaptations.
One of the standout elements of this paper is the Combiner network. This network adeptly amalgamates the features extracted by the fine-tuned CLIP encoders, producing a more insightful representation that is conducive to successful image retrieval. Its architecture, which incorporates learning a residual of a convex combination of image-text features, reflects a comprehensive understanding of the importance of distinguishing between visual and textual contributions to retrieval tasks.
The empirical results presented are compelling. On the complex datasets FashionIQ and CIRR, which present significant challenges due to their diversity and the intricacies involved in the queries, the proposed approach demonstrates a remarkable improvement in retrieval performance. When comparing models with a ResNet-50 backbone, the proposed method achieved significant gains in Recall metrics, outperforming state-of-the-art competitors such as FashionViL and CIRPLANT across all metrics. The scalability of the approach is emphasized with the RN-50x4 backbone, yielding even further improvements.
The theoretical implications of this work are profound. By enhancing the additivity properties of the embedding spaces, this approach circumvents some inherent limitations of unified embedding approaches in composed image retrieval, setting a precedent for future research to focus on complementary and dynamic feature interactions in multimodal datasets. Additionally, the preprocess pipeline introduced to deal with varying aspect ratios of images ensures minimal information loss, showcasing a method with potential applicability across various domains where image quality and detail are paramount.
In summary, this paper contributes significantly to the domain of composed image retrieval by integrating a robust fine-tuning methodology with a sophisticated feature combination strategy. Future work could explore integrating domain-specific knowledge into the pre-training phase of CLIP or similar models. The findings herein are likely to inform subsequent developments in the field, as the balance between visual and textual elements in retrieval tasks becomes an increasingly pivotal area of research.