Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V (2310.11441v2)

Abstract: We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V. As illustrated in Fig. 1 (right), we employ off-the-shelf interactive segmentation models, such as SEEM/SAM, to partition an image into regions at different levels of granularity, and overlay these regions with a set of marks e.g., alphanumerics, masks, boxes. Using the marked image as input, GPT-4V can answer the questions that require visual grounding. We perform a comprehensive empirical study to validate the effectiveness of SoM on a wide range of fine-grained vision and multimodal tasks. For example, our experiments show that GPT-4V with SoM in zero-shot setting outperforms the state-of-the-art fully-finetuned referring expression comprehension and segmentation model on RefCOCOg. Code for SoM prompting is made public at: https://github.com/microsoft/SoM.

Essay on "Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V"

The paper "Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V" introduces the Set-of-Mark (SoM) prompting mechanism, an innovative approach designed to enhance the visual grounding capabilities of Large Multimodal Models (LMMs), specifically focusing on GPT-4V. The method demonstrates significant improvement in handling fine-grained vision tasks that traditionally necessitate precise spatial understanding and rich semantic comprehension.

Introduction and Motivation

The burgeoning interest in LMMs, notably with the release of GPT-4V, underscores the industry's and academia's growing commitment to developing models proficient in multimodal perception and reasoning. However, despite their notable advancements, existing LMMs face challenges in fine-grained visual grounding tasks, such as predicting accurate coordinates for objects within an image. The motivation behind this paper is to address these limitations and improve the spatial understanding capabilities of GPT-4V.

Set-of-Mark (SoM) Prompting

The core innovation presented in the paper is the Set-of-Mark (SoM) prompting method. The technique involves partitioning an image into semantically meaningful regions using off-the-shelf interactive segmentation models like SEEM and SAM. Each region is then overlaid with distinct marks such as alphanumeric characters, masks, or boxes. These marked images serve as inputs to GPT-4V, enabling it to process and respond to queries requiring high-level visual grounding.

Methodology

The methodology consists of several key components:

1. Image Partitioning: Utilizing advanced segmentation models (e.g., MaskDINO, SEEM, SAM), the input image is divided into regions of interest that convey rich semantic and spatial information.
2. Set-of-Mark Generation: After partitioning, visually distinct marks are overlaid on these regions. These marks are selected for their interpretability and "speakability" by LMMs, ensuring that GPT-4V can recognize and correctly associate them with textual responses.
3. Interleaved Prompting: Two types of textual prompts are used: plain text prompts and interleaved text prompts. The latter incorporates the visual marks directly into the text to enhance the grounding performance.

Empirical Validation

The paper provides a comprehensive empirical paper validating the effectiveness of SoM across various fine-grained vision tasks. Notably, SoM-enhanced GPT-4V outperforms state-of-the-art, fully-finetuned referring expression comprehension and segmentation models in a zero-shot setting. The improvements are highlighted through strong numerical results on tasks such as open-vocabulary segmentation, referring segmentation, phrase grounding, and video object segmentation.

For example, in the task of referring segmentation on RefCOCOg, SoM-prompted GPT-4V achieves a superior mean Intersection over Union (mIoU) compared to specialist models like PolyFormer and SEEM. Moreover, in phrase grounding on the Flickr30K dataset, SoM enhances GPT-4V to perform on par with well-established models such as GLIPv2 and Grounding DINO.

Implications and Future Directions

The practical implications of this research are substantial. By leveraging SoM, one can significantly enhance the visual reasoning and interactive capabilities of LMMs in real-world applications such as human-AI interaction, autonomous systems, and detailed image annotations. Furthermore, the paper's findings pave the way for future developments in AI, where combining robust visual and language promptings could unlock new potentials in multimodal intelligence.

Theoretically, the research opens new avenues in prompt engineering, showing that simple visual promptings can effectively bridge the gap between semantic and spatial understanding in multimodal models. This becomes particularly evident as SoM enables GPT-4V to produce outputs that link visual regions to textual descriptions seamlessly.

Conclusion

The paper "Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V" showcases that augmenting images with interpretable visual marks remarkably enhances the grounding abilities of GPT-4V, thus overcoming significant challenges faced in fine-grained vision tasks. Through meticulous empirical evaluations, the paper illustrates the robustness of SoM, setting a foundation for future research aimed at refining multimodal interactions in AI systems. The results hold promising implications for advancing the capabilities of LMMs and broadening their applicability across diverse real-world scenarios.