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Attention Prompting on Image for Large Vision-Language Models: A New Approach to Enhance Visual Understanding

Large vision-language models (LVLMs) are increasingly adept at performing tasks that require them to understand both visual and linguistic information. However, existing visual prompting techniques tend to focus solely on visual inputs, neglecting the crucial role of textual queries. This can lead to a mismatch between the visual prompts provided and the instructions contained in the text, ultimately hindering the model’s ability to fulfill the task.

To address this limitation, researchers have proposed a novel technique called Attention Prompting on Image (API), which leverages an auxiliary LVLM to generate a text-query-guided attention heatmap. This heatmap is overlaid on the original input image, effectively highlighting areas relevant to the query. The resulting image is then fed to the main LVLM for analysis, thus enhancing its ability to focus on the most pertinent visual information.

Imagine you’re asking an LVLM to identify the fruit in the left part of a fridge. Without API, the model might be distracted by all the different fruits in the image, making it difficult to pinpoint the specific one you’re asking about. API overcomes this by generating a heatmap that highlights the left side of the fridge, guiding the model’s attention to the relevant area and enabling it to provide a more accurate answer.

API has been shown to significantly improve performance on various vision-language benchmarks, including:

Extensive experimentation has demonstrated the effectiveness of API across a range of LVLMs, including CLIP and LLaVA, and across various tasks. The authors have also conducted ablation studies to identify key factors that influence the performance of API, including the scale and depth of the auxiliary LVLM, the kernel size of the mean filter used to smooth the heatmap, and the specific transformer layer used to extract the attribution map.

API represents a significant advancement in the field of visual prompting. It addresses a major limitation of existing visual prompting techniques, thereby improving the ability of LVLMs to effectively incorporate textual instructions into their visual analysis. As research in the field of large vision-language models continues to advance, API holds immense promise for enhancing the capabilities of these models, enabling them to achieve even greater levels of visual understanding.