In this study, we present a novel evaluation framework for image-captioning models that integrate statistical analysis with common evaluation metrics, utilizing two popular datasets, FashionGen and Amazon, with contrasting dataset variation to evaluate four models: Video-LLaVa, BLIP, CoCa and ViT-GPT2. Our approach not only reveals the comparative strengths of models, offering insights into their adaptability and applicability in real-world scenarios but also contributes to the field by providing a comprehensive evaluation method that considers both statistical significance and practical relevance to guide the selection of models for specific applications. Specifically, we propose Rank Score as a new evaluation metric that is designed for e-commerce image search applications and employ CLIP Score to quantify dataset variation to offer a holistic view of model performance.

This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model. The method aims to address the limitations of the bi-encoder architecture while maintaining a minimal additional training burden. The approach involves labeling top products for each query, generating semantically similar query clusters using the K-Means clustering algorithm, and fine-tuning a global language model into cluster language models on individual clusters. The parameters of each cluster language model are fine-tuned to learn local manifolds in the feature space efficiently, capturing the nuances of various query types within each cluster. The inference is performed by assigning a new query to its respective cluster and utilizing the corresponding cluster language model for retrieval. The proposed method results in more accurate and personalized retrieval results, offering a superior alternative to the popular bi-encoder based retrieval models in semantic search.