HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities
📰 ArXiv cs.AI
Learn how to leverage Hard Negative Captions (HNC) to improve models' fine-grained visual-linguistic comprehension capabilities in Image-Text-Matching (ITM) tasks
Action Steps
- Collect a large corpus of image-text pairs from the web
- Create a dataset of Hard Negative Captions (HNC) using automated methods
- Fine-tune a pre-trained model on the HNC dataset to improve its visual-linguistic comprehension capabilities
- Evaluate the model's performance on ITM tasks using metrics such as accuracy and F1-score
- Compare the results with baseline models to demonstrate the effectiveness of HNC
Who Needs to Know This
AI researchers and engineers working on multimodal models can benefit from this technique to enhance their models' performance in ITM tasks
Key Insight
💡 Using HNC can help models develop a more nuanced understanding of the combined semantics of image-text pairs
Share This
🚀 Improve your multimodal models with Hard Negative Captions (HNC) for fine-grained visual-linguistic comprehension! #AI #MultimodalLearning
Key Takeaways
Learn how to leverage Hard Negative Captions (HNC) to improve models' fine-grained visual-linguistic comprehension capabilities in Image-Text-Matching (ITM) tasks
Full Article
Title: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities
Abstract:
arXiv:2605.06157v1 Announce Type: cross Abstract: Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image-text pairs, models fail to show a fine-grained understanding of the combined semantics of these modalities. To address this issue we propose Hard Negative Captions (HNC): an automatically created dataset containing foiled hard negative
Abstract:
arXiv:2605.06157v1 Announce Type: cross Abstract: Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image-text pairs, models fail to show a fine-grained understanding of the combined semantics of these modalities. To address this issue we propose Hard Negative Captions (HNC): an automatically created dataset containing foiled hard negative
DeepCamp AI