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Fine-Grained Visual Prompting (2306.04356v2)

Published 7 Jun 2023 in cs.CV

Abstract: Vision-LLMs (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited performance in instance-level tasks that demand precise localization and recognition. Previous works have suggested that incorporating visual prompts, such as colorful boxes or circles, can improve the ability of models to recognize objects of interest. Nonetheless, compared to language prompting, visual prompting designs are rarely explored. Existing approaches, which employ coarse visual cues such as colorful boxes or circles, often result in sub-optimal performance due to the inclusion of irrelevant and noisy pixels. In this paper, we carefully study the visual prompting designs by exploring more fine-grained markings, such as segmentation masks and their variations. In addition, we introduce a new zero-shot framework that leverages pixel-level annotations acquired from a generalist segmentation model for fine-grained visual prompting. Consequently, our investigation reveals that a straightforward application of blur outside the target mask, referred to as the Blur Reverse Mask, exhibits exceptional effectiveness. This proposed prompting strategy leverages the precise mask annotations to reduce focus on weakly related regions while retaining spatial coherence between the target and the surrounding background. Our Fine-Grained Visual Prompting (FGVP) demonstrates superior performance in zero-shot comprehension of referring expressions on the RefCOCO, RefCOCO+, and RefCOCOg benchmarks. It outperforms prior methods by an average margin of 3.0% to 4.6%, with a maximum improvement of 12.5% on the RefCOCO+ testA subset. Code is available at https://github.com/ylingfeng/FGVP.

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Citations (43)
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Summary

  • The paper presents the Blur Reverse Mask, a novel approach that refines visual prompts by blurring non-target areas to improve spatial localization in VLMs.
  • The methodology employs pixel-level segmentation masks instead of coarse markers, significantly reducing irrelevant pixel inclusion and enhancing prompt precision.
  • Experimental results demonstrate FGVP’s effectiveness, achieving up to a 12.5% accuracy improvement on key benchmarks like RefCOCO, RefCOCO+, and RefCOCOg.

Fine-Grained Visual Prompting: Enhancing Vision-LLM Performance on Instance-Level Tasks

The paper "Fine-Grained Visual Prompting" addresses a notable gap in the application of Vision-LLMs (VLMs), such as CLIP, which traditionally exhibit limitations in tasks requiring detailed spatial localization and recognition. While VLMs demonstrate commendable zero-shot transfer capacities in general image-level perception, their efficacy diminishes in more nuanced instance-level tasks. This paper rigorously investigates the design and optimization of visual prompts, proposing an innovative framework for enhancing VLM performance in such tasks.

Key Developments and Contributions

  1. Current Limitations in Visual Prompting: The paper begins by critically examining existing visual prompting techniques, which primarily use simplistic and coarse visual markers—such as colorful boxes or circles—to direct model focus. These methods often underperform due to their imprecision and the excessive inclusion of non-essential pixel data.
  2. Innovation in Prompting Techniques: To counter these limitations, the researchers propose using more sophisticated visual prompts like segmentation masks and their derivatives. By introducing pixel-level annotations from generalist segmentation models, they develop a methodology dubbed the Blur Reverse Mask. This mask blurs areas outside the target region, fostering better spatial attention by minimizing the inclusion of irrelevant regions.
  3. Experimental Validation: The paper reports that the Fine-Grained Visual Prompting (FGVP) framework significantly surpasses previous methodologies on benchmarks such as RefCOCO, RefCOCO+, and RefCOCOg. It achieves accuracy improvements ranging from 3.0% to 4.6% on average, with a remarkable enhancement of 12.5% on specific dataset subsets. These results underscore the efficacy of FGVP, particularly in understanding referring expressions and part detection tasks.
  4. Deployment and Framework: The paper not only suggests new visualization strategies but also outlines a zero-shot classification pipeline that leverages FGVP. The effectiveness of the proposed method is empirically validated by improvements across various datasets, emphasizing its robustness in real-world scenarios.

Practical and Theoretical Implications

From a practical standpoint, Fine-Grained Visual Prompting addresses key challenges faced in deploying VLMs for applications that necessitate precise object localization and context comprehension. This advancement potentially streamlines tasks in image editing, open vocabulary detection, and more, offering a robust solution adaptable across diverse real-world scenarios.

Theoretically, the research explores the underexplored domain of visual prompt engineering within VLMs, specifically evaluating the impact of prompt precision on model performance. It poses intriguing questions about the potential for further refinement in VLM contextual learning without extensive dataset-specific retraining. This paper invites further exploration into the intersection of fine-grained vision cues and language-based models, encouraging the development of integrated frameworks capable of more complex semantic understanding.

Future Directions

Looking ahead, this research opens several avenues for further paper. Understanding the impact of alternative fine-grained visual markers and their potential combinations with language prompts could pave the way for even more nuanced model enhancements. Additionally, exploring the scalability of these findings across different types of VLMs could help in developing universally applicable strategies to improve instance-level task performance.

In conclusion, the paper makes a substantial contribution to the field by advancing our understanding of how to enhance VLM performance on detailed visual tasks through innovative visual prompting techniques. The success of FGVP in empirical evaluations demonstrates its potential to be a powerful tool in the arsenal of machine learning practitioners and researchers focused on combining visual and language insights.

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  1. GitHub - ylingfeng/FGVP: Official Codes for Fine-Grained Visual Prompting, NeurIPS 2023 (53 stars)
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