How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Recent advancements in artificial intelligence have led to the development of Multimodal Foundation Models (MFMs), such as GPT-4o, which integrate text and visual information. While these models have demonstrated impressive capabilities, a comprehensive understanding of their visual processing abilities remains a topic of ongoing research. A new paper, available on arXiv (2507.01955v3), benchmarks several MFMs against standard computer vision tasks to evaluate their performance.
Benchmarking Methodology
The study assesses a range of popular MFMs, including:
- GPT-4o
- o4-mini
- Gemini 1.5 Pro
- Gemini 2.0 Flash
- Claude 3.5 Sonnet
- Qwen2-VL
- Llama 3.2
The evaluation focuses on well-established computer vision tasks, such as:
- Semantic segmentation
- Object detection
- Image classification
- Depth and surface normal prediction
To conduct this analysis effectively, the authors faced several challenges. One major hurdle was that many models are primarily designed for text output, limiting their ability to represent complex visual domains like segments or 3D geometry. Additionally, many leading models are proprietary, with access restricted to API-level interactions, preventing researchers from directly manipulating model weights. To overcome these obstacles, the authors translated vision tasks into text-promptable formats compatible with APIs using prompt chaining, thereby creating a standardized benchmarking framework.
Key Findings
The results of the benchmarking revealed several insights into the capabilities of MFMs:
- Performance Gap: MFMs did not match the performance of state-of-the-art specialist models across any of the evaluated tasks.
- Generalist Abilities: Despite the performance gap, the MFMs displayed respectable generalist capabilities, which is noteworthy given their training predominantly involved image-text tasks.
- Semantic vs. Geometric Tasks: The models excelled in semantic tasks but struggled with geometric tasks.
- Top Performer: Among non-reasoning models, GPT-4o emerged as the best performer, leading in 4 out of the 6 tasks assessed.
- Reasoning Models: Reasoning-focused models, such as o3, showed improvement in geometric tasks, suggesting that reasoning capabilities may enhance performance in those areas.
- Prompt Sensitivity: Although prompt chaining techniques influenced performance outcomes, higher-quality models exhibited less sensitivity to prompt variations.
- Failure Modes: An analysis of models capable of native image generation, particularly GPT-4o, revealed failure modes, including hallucinated objects and misalignment between input and output.
Conclusion
The study underscores the potential of MFMs like GPT-4o in bridging the gap between text and visual understanding while also highlighting limitations in their current capabilities. As the field progresses, further research will be essential to enhance these models’ performance in complex visual tasks, particularly in geometric understanding, thereby unlocking new applications across various domains.
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