Learn PromptingAttention Prompting on Image
Last updated on October 1, 2024
- Overview of Attention Prompting: Attention Prompting on Image enhances Vision-Language Models (VLMs) by overlaying text-guided heatmaps on images to direct model focus.
- Process Description: The technique generates a heatmap from an auxiliary model, highlights relevant image areas, and uses this modified image for improved answer generation.
- Performance Improvement: Attention Prompting significantly boosts accuracy across various benchmarks, demonstrating effective enhancements in VLM performance on vision-language tasks.
What is Attention Prompting on Image?
Attention Prompting on Image is a novel technique designed to enhance the performance of Vision-Language Models (VLMs) on vision-language tasks. It does this by overlaying a text-query-guided attention heatmap on the original image, which helps guide the model's focus to relevant areas of the image based on the specific question or task at hand.
How Attention Prompting on Image Works
- Text-guided Heatmap: An auxiliary VLM generates an attention heatmap based on the input text query, highlighting parts of the image that are relevant to answering the question.
- Image Overlay: The heatmap is applied to the image, modifying it to highlight important areas. This modified image is then used as input for the main VLM.
- Answer Generation: The VLM uses this focused image to answer questions more accurately.
How to Use Attention Prompting on Image
Attention Prompting on Image can be integrated into any VLM pipeline that processes both image and text. Below is a simplified workflow:
- Original Image: Provide the image and text query to an auxiliary model like CLIP or LLaVA.
- Heatmap Generation: The auxiliary model creates a heatmap indicating which areas of the image are most relevant to the query.
- Image Modification: Overlay the heatmap onto the image to emphasize key regions.
- Final Inference: Feed the modified image and the original query to the main VLM to generate a more accurate response.
For implementation, you can check the original open-source code on GitHub.
Results of Attention Prompting on Image
Attention Prompting on Image significantly improves performance on several benchmarks by helping the VLM focus on relevant parts of the image based on the text query.
Key Performance Gains:
- LLaVA-1.5: Attention Prompting on Image improves accuracy by 3.8% on the MM-Vet dataset and 2.9% on the LLaVA-Wild benchmark.
- GPT-4V: With Attention Prompting on Image, GPT-4V gains up to 11.6% improvement in accuracy on certain vision-language tasks.
Benchmark Results:
| Model | Dataset | No Prompt (%) | Attention Prompting on Image (%) | Improvement (%) |
|---|---|---|---|---|
| LLaVA-1.5 | MM-Vet | 32.8 | 36.6 | +3.8 |
| LLaVA-1.5 | LLaVA-Bench | 71.9 | 74.8 | +2.9 |
| GPT-4V | VisWiz | 59.4 | 71.01 | +11.6 |
Conclusion
Attention Prompting on Image (Attention Prompting on Image) enhances the performance of LVLMs by providing query-specific attention heatmaps, which help the models better interpret and respond to vision-language tasks. It improves accuracy across a range of benchmarks, reducing hallucination and enhancing self-reflection. Attention Prompting on Image opens new avenues for using visual signals to improve vision-language models without requiring complex training or fine-tuning.