Omni-NegCLIP: Enhancing CLIP with Front-Layer Contrastive Fine-Tuning for Comprehensive Negation Understanding
#Omni-NegCLIP #CLIP #contrastive fine-tuning #negation understanding #vision-language models #multimodal AI #front-layer tuning
📌 Key Takeaways
- Omni-NegCLIP improves CLIP's ability to understand negation in images and text.
- It uses front-layer contrastive fine-tuning for enhanced performance.
- The method addresses limitations in existing vision-language models regarding negation.
- It aims for more comprehensive and accurate multimodal understanding.
📖 Full Retelling
arXiv:2603.29258v1 Announce Type: cross
Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that VLMs, such as CLIP, perform poorly in understanding negation expressions, which are common in natural language. In this work, we propose Omni-NegCLIP, a fine-tuned CLIP model that improves CLIP's understanding of two types of negation, namely presence-based negation and absence-based negation, which
🏷️ Themes
AI Enhancement, Negation Understanding
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Original Source
arXiv:2603.29258v1 Announce Type: cross
Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that VLMs, such as CLIP, perform poorly in understanding negation expressions, which are common in natural language. In this work, we propose Omni-NegCLIP, a fine-tuned CLIP model that improves CLIP's understanding of two types of negation, namely presence-based negation and absence-based negation, which
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