Edge Reliability Gap in Vision-Language Models: Quantifying Failure Modes of Compressed VLMs Under Visual Corruption
📰 ArXiv cs.AI
Study quantifies failure modes of compressed vision-language models under visual corruption, revealing differences in error patterns between compact and large models
Action Steps
- Identify the error taxonomy for vision-language models, including Object Blindness, Semantic Drift, and Prior Bias
- Compare the performance of compact models (e.g., Qwen2.5-VL-7B) with larger models (e.g., SmolVLM2-500M) under visual corruption
- Analyze the failure modes of compressed models using a dataset of 4,000 samples from VQAv2 and COCO Captions
- Develop strategies to mitigate the edge reliability gap in vision-language models, such as data augmentation and robustness techniques
Who Needs to Know This
AI engineers and researchers working on vision-language models can benefit from this study to improve the reliability of their models, especially when deploying them on edge devices
Key Insight
💡 Compact vision-language models exhibit distinct error patterns compared to larger models, highlighting the need for tailored robustness techniques
Share This
💡 Compressed vision-language models fail differently, not just more often, under visual corruption #AI #VLM
Key Takeaways
Study quantifies failure modes of compressed vision-language models under visual corruption, revealing differences in error patterns between compact and large models
Full Article
Title: Edge Reliability Gap in Vision-Language Models: Quantifying Failure Modes of Compressed VLMs Under Visual Corruption
Abstract:
arXiv:2603.26769v1 Announce Type: cross Abstract: The rapid compression of large vision-language models (VLMs) for edge deployment raises an underexplored question: do compact models fail differently, not merely more often? This study compares a 7-billion-parameter quantised VLM (Qwen2.5-VL-7B, 4-bit NF4) against a 500-million-parameter FP16 model (SmolVLM2-500M) across 4,000 samples from VQAv2 and COCO Captions. A three-category error taxonomy (Object Blindness, Semantic Drift, Prior Bias) is a
Abstract:
arXiv:2603.26769v1 Announce Type: cross Abstract: The rapid compression of large vision-language models (VLMs) for edge deployment raises an underexplored question: do compact models fail differently, not merely more often? This study compares a 7-billion-parameter quantised VLM (Qwen2.5-VL-7B, 4-bit NF4) against a 500-million-parameter FP16 model (SmolVLM2-500M) across 4,000 samples from VQAv2 and COCO Captions. A three-category error taxonomy (Object Blindness, Semantic Drift, Prior Bias) is a
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