Can Argus Judge Them All? Comparing VLMs Across Domains

2025-06-23

AI Summary

This work systematically evaluates the cross-task performance consistency of prominent vision-language models (VLMs)—including CLIP, BLIP, and LXMERT—across image retrieval, caption generation, and visual reasoning. It exposes an inherent trade-off between generalization capability and task-specific specialization. To quantify model stability, we propose a novel metric: Cross-Dataset Consistency (CDC), integrated with multi-dimensional evaluation of accuracy, generation quality, and reasoning efficiency. Experimental results show that CLIP achieves the highest generalization (CDC = 0.92), BLIP excels in accuracy on specific tasks, and LXMERT attains superior performance in structured visual reasoning. Crucially, this study introduces consistency modeling into VLM evaluation for the first time, establishing a quantifiable framework for balancing generality versus specialization—thereby providing principled, actionable guidance for model selection and architecture design in industrial applications.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Vision-Language Models (VLMs) are advancing multimodal AI, yet their performance consistency across tasks is underexamined. We benchmark CLIP, BLIP, and LXMERT across diverse datasets spanning retrieval, captioning, and reasoning. Our evaluation includes task accuracy, generation quality, efficiency, and a novel Cross-Dataset Consistency (CDC) metric. CLIP shows strongest generalization (CDC: 0.92), BLIP excels on curated data, and LXMERT leads in structured reasoning. These results expose trade-offs between generalization and specialization, informing industrial deployment of VLMs and guiding development toward robust, task-flexible architectures.

Problem

• Evaluating VLM performance consistency across diverse tasks
• Comparing CLIP, BLIP, LXMERT on accuracy and generalization
• Analyzing trade-offs between generalization and specialization in VLMs

Innovation

• Benchmarking CLIP, BLIP, LXMERT across diverse datasets
• Introducing Cross-Dataset Consistency (CDC) metric
• Analyzing trade-offs between generalization and specialization