Multimodal LLMs in Anomaly Detection: Is the Extra Complexity Worth It Compared to Pure Vision Models?

Multimodal LLMs for anomaly detection are sparking debate: does adding text understanding actually help when you’re just spotting scratches and cracks in images? A Reddit thread weighs the trade-offs, pitting a multimodal path around Gemma3:4b against straight-up vision baselines ^[1].

Usefulness of multimodal LLMs for anomaly detection

For image-only anomalies, the post says the multimodal angle may not buy much—dense captions are rare in this setting ^[1]. That skepticism fuels the question: is the extra complexity and data really worth it when the core signal is visual?

Baselines and evaluation

A practical takeaway is that a reasonable baseline is fine-tuning small, normal- or small-vision models; such baselines may perform similarly to multimodal prototypes at a fraction of compute ^[1]. The thread also voices a warning: a VLLM can be overkill and unreliable in production ^[1].

Practical tooling and checks

If you still explore multimodal paths, check anomalib—the post notes you can train a model in around half an hour and it will run in real time ^[1]. There’s also a nudge to look up domain-specific methods via Google Scholar for the right metrics and baselines ^[1].

Closing thought

The takeaway: multimodal LLM approaches are an intriguing experiment, but pure vision baselines remain strong contenders for anomaly detection today ^[1].

References