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About this paper
Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, Xin Geng
Researchers from Southeast University systematically explored diverse in-context configurations for few-shot image captioning, revealing how image and caption qualities uniquely interact to influence Vision-Language Model performance. The study introduced practical strategies like using model-generated captions as anchors, which boosted CIDEr scores by an average of 20.9 points over random baselines.
Key insights
- The influence of caption descriptiveness and language patterns on in-context performance is unequal; simpler, consistent language patterns can be more effective than richer ground-truth captions when visual cues are strong.
- The efficacy of selecting visually similar images is critically dependent on the quality of their paired captions; low-quality captions can lead to performance degradation with similar images due to "short-cut inference."
- VLMs can exhibit "short-cut inference," directly leveraging in-context captions for similar images rather than genuinely learning visual-linguistic mappings, which can be detrimental with poor-quality captions.
Summary by alphaXiv.
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