arXiv:2606.01710v1 Announce Type: cross Abstract: Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification. However, their predictions remain sensitive to spurious correlations, where contextual cues dominate over semantic content. Earlier solutions typically rely on fine-tuning or prompt engineering, which either undermine the advantages of pre-trained models or are prone to hallucination. In this work, we propose Density-Aware Translation (DAT) that refines image-text similarity scores using a local geometric density term derived from group reference sets. Our a
The paper addresses a crucial limitation in zero-shot VLMs, which are becoming ubiquitous, highlighting an ongoing push to refine AI model robustness and reliability.
Improving the accuracy and robustness of Vision-Language Models mitigates risks associated with biased AI predictions and expands their reliable application across various domains.
Zero-shot VLM predictions can be more accurately refined by considering local geometric density, reducing sensitivity to spurious correlations without costly fine-tuning.
- · AI developers
- · Industries relying on VLM for classification
- · AI research community
- · Platforms with unmitigated VLM biases
- · Approaches solely reliant on prompt engineering
More reliable and less biased zero-shot VLM applications across various sectors are enabled.
This improved reliability could accelerate VLM adoption in critical decision-making systems.
Increased trust in AI models might lead to broader societal integration of AI, potentially affecting labor markets and expert systems.
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Read at arXiv cs.LG