Evaluation of Vision Encoder Models for Image Retrieval Tasks

Published on 2025-11-12 • Avichala Research

Evaluation of Vision Encoder Models for Image Retrieval Tasks

Abstract: This research investigates the performance of various vision encoder models – including OpenCLIP, FastCLIP, and novel models like CC3M, CC12M, and AmorLIP – across a range of image retrieval tasks. The study systematically evaluates these models on diverse datasets, comparing their effectiveness in retrieving relevant images based on textual queries. The core focus is on quantifying the differences in retrieval accuracy and identifying the most efficient models for this critical application, with implications for multimodal AI systems and advanced image search technologies.

Problem Statement: The increasing prevalence of multimodal AI systems, particularly those designed for image understanding and retrieval, necessitates robust and accurate vision encoder models. Current challenges revolve around the selection of optimal encoder architectures for diverse image retrieval scenarios – from general knowledge searches to highly specific queries. Furthermore, the significant variation in model sizes and training methodologies introduces complexity in determining the trade-offs between accuracy and computational cost. This work addresses the critical need for a systematic evaluation framework to identify the most effective vision encoders for image retrieval, directly impacting applications such as visual question answering, image search engines, and multimodal AI agents. The research aims to inform the design and development of more efficient and effective systems capable of seamlessly integrating visual and textual information.

Methodology: The study employs a comprehensive evaluation methodology involving multiple vision encoder models and a consistent experimental setup. The core models tested include: CC3M, CC12M, DFN-14M, DFN-192M, DFN-1B, OpenCLIP 21.84, FastCLIP, SigLIP, AmorLIP, and NeuCLIP. Each model was subjected to rigorous testing using both standardized and internally developed datasets. The experiments were conducted across three key dimensions: model size, dataset, and training paradigm (Time, Tu, and Tr).

• Model Selection: The research utilized a spectrum of vision encoder architectures, ranging from smaller models (CC3M, CC12M) to larger, more computationally intensive ones (DFN-1B). This allowed for a comparative analysis of the relationship between model size and retrieval performance. Importantly, it includes the inclusion of CLIP variants (OpenCLIP) and specialized retrieval-focused models (FastCLIP, SigLIP, AmorLIP, NeuCLIP), offering insights into the effectiveness of models specifically designed for this task.
• Datasets: The experiments leveraged a combination of publicly available datasets, notably CC3M and CC12M, along with internal datasets for increased coverage and controlled experimentation. Datasets were used to define image categories and create query sets.
• Training Paradigm: The experiments incorporated various training configurations (Time, Tu, and Tr) allowing for a controlled analysis of training methodologies' impact.
• Evaluation Metrics: The primary evaluation metric was image retrieval accuracy, measured using standard metrics commonly employed in information retrieval. The data presented includes average precision, recall, and F1-score—although the specific metric wasn't explicitly detailed. Notably, performance was evaluated across a broad range of image retrieval configurations (Time, Tu, and Tr).

Findings & Results: The research consistently demonstrated a positive correlation between model size and retrieval accuracy. Larger models, such as DFN-1B and OpenCLIP, consistently outperformed smaller models across all datasets. Specifically, OpenCLIP demonstrated the highest average retrieval accuracy, exceeding 54% on the evaluation datasets. FastCLIP and SigLIP also yielded competitive performance, particularly with model CC3M and CC12M. Importantly, the experiments highlighted the sensitivity of model performance to the specific training configuration. The highest retrieval accuracy was observed with OpenCLIP 21.84 and FastCLIP at 54.58% and 54.72% respectively. The variations in Time, Tu, and Tr configurations resulted in varied performance metrics for each model, showcasing the adaptability of these architectures.

Limitations: The study is subject to several limitations. The evaluation primarily focuses on the models and datasets presented, potentially overlooking other promising architectures or datasets. The specific details of the training data and optimization procedures utilized are not fully disclosed, limiting the ability to fully reproduce or validate the results. Furthermore, the reliance on standard image retrieval metrics may not fully capture the nuanced aspects of human visual perception and understanding. The study offers a snapshot of the performance at a specific point in time and may be influenced by the rapidly evolving landscape of vision models. The absence of a thorough analysis of the underlying query-image relationship adds to this limitation.

Future Work & Outlook: This research provides a solid foundation for future investigations in vision encoder model evaluation. Future work should explore a wider range of vision architectures, including transformer-based models beyond CLIP. Furthermore, research could delve deeper into the impact of different query formulation techniques and the development of more sophisticated evaluation metrics that align better with human judgment. Investigating the effect of few-shot learning and transfer learning on these models presents a valuable avenue for exploration. Moreover, exploring the integration of these vision encoders with larger language models (LLMs) to create more powerful multimodal AI agents is a highly promising direction. The future will undoubtedly see continued advancements in vision encoders, driven by the ongoing quest for more efficient, accurate, and adaptable systems capable of seamlessly understanding and interacting with the visual world.

Avichala Commentary: This study is a crucial step in the ongoing evolution of multimodal AI. The shift towards larger, more capable vision encoders, exemplified by the performance of models like OpenCLIP and DFN-1B, mirrors the broader trend in LLMs – scaling up model size to unlock greater potential. The research underscores the importance of rigorous benchmarking, a trend increasingly recognized across the AI research community. This investigation directly contributes to the development of more effective visual reasoning capabilities, a key component in creating truly intelligent agents capable of operating in complex, real-world environments. The findings contribute to the wider AI field’s progress as we move toward seamless multimodal understanding and interaction—essential for building agents capable of real-world problem solving.

Link to the Arxiv: https://arxiv.org/abs/2511.08417v1.pdf