BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models (2312.02896v2)

Abstract: Large Multimodal Models (LMMs) such as GPT-4V and LLaVA have shown remarkable capabilities in visual reasoning with common image styles. However, their robustness against diverse style shifts, crucial for practical applications, remains largely unexplored. In this paper, we propose a new benchmark, BenchLMM, to assess the robustness of LMMs against three different styles: artistic image style, imaging sensor style, and application style, where each style has five sub-styles. Utilizing BenchLMM, we comprehensively evaluate state-of-the-art LMMs and reveal: 1) LMMs generally suffer performance degradation when working with other styles; 2) An LMM performs better than another model in common style does not guarantee its superior performance in other styles; 3) LMMs' reasoning capability can be enhanced by prompting LMMs to predict the style first, based on which we propose a versatile and training-free method for improving LMMs; 4) An intelligent LMM is expected to interpret the causes of its errors when facing stylistic variations. We hope that our benchmark and analysis can shed new light on developing more intelligent and versatile LMMs.

BenchLMM: Evaluating Cross-style Robustness in Large Multimodal Models

The exploration of Large Multimodal Models (LMMs) has surged in recent years, driven by their capacity to integrate and analyze visual and textual data. Models such as GPT-4V have demonstrated proficiency in understanding images in common styles. However, their efficacy in processing images from diverse styles—artistic, sensor, and application—remains unclear. The paper "BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models" introduces a comprehensive evaluation benchmark known as BenchLMM, aimed at assessing this very aspect.

Overview of BenchLMM

BenchLMM addresses three pivotal types of style shifts that influence LMMs: artistic, sensor, and application styles. Each type incorporates substyles to ensure a robust evaluation. For instance, artistic style shifts examine the model's performance on paintings, sketches, cartoons, handmade, and tattoo images. Sensor shifts focus on non-RGB images—such as infrared and X-ray—while application style tests require domain-specific knowledge for tasks like autonomous driving or remote sensing.

Key Findings

The authors present several significant insights based on their evaluation using BenchLMM:

1. Performance Degradation in Diverse Styles: LMMs exhibit notable performance drops when encountering images not aligned with their usual training data. Even models with outstanding capabilities in common styles showed reduced accuracy across other styles, implying potential overfitting to familiar visual distributions.
2. Misleading Indicators of Model Superiority: An LMM outperforming others in common styles does not imply better adaptability or robustness in other styles—highlighting the need for comprehensive evaluations beyond common benchmarks.
3. Style Prompt Enhancement (SPE): Introducing prompts that encourage LMMs to predict the style before answering questions enhances reasoning capabilities significantly. SPE acts as a versatile, training-free augmentation strategy that boosts performance across various styles.
4. Error-Reflection Analysis: The ability of an LMM to interpret the causes of its errors varies significantly among models. Particularly, more intelligent models like GPT-4V can derive insights and learn from corrections, a capability crucial yet somewhat overlooked in current LMM development and evaluation.

Implications for Future Research

The implications of this research are profound for both theoretical exploration and practical application within AI domains. By elucidating the limitations of current models, it paves the way for developing LMMs that are genuinely versatile and robust across multifaceted real-world scenarios. Moreover, the introduction of SPE suggests that even simple enhancements can yield considerable improvements in model adaptability, offering another dimension for optimizing LMMs without intensive computational retraining.

The examination of error-reflection capabilities offers a novel perspective on model sophistication—emphasizing that true intelligence in LMMs also encompasses self-awareness and adaptive learning mechanisms. Future work in AI can leverage these insights to refine models, ensuring not only accuracy but also adaptability and self-improvement.

Conclusion

BenchLMM provides a foundational tool for evaluating the robustness of LMMs, shining light on the necessity of diverse benchmarks away from common style biases. This paper advocates for a shift in perspective—toward cultivating models that understand and adapt to stylistic nuances and learn from their experiences. Consequently, it enriches the discourse on LMM development, emphasizing comprehensive evaluation strategies and error-reflection capabilities critical to evolving more nuanced models. The path ahead involves an intricate balance between innovation in model design, training data diversity, and strategic evaluations that embrace the complexities of real-world applications.