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InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding

Huaxiang Zhang, Yaojia Mu, Guo-Niu Zhu, Zhongxue Gan

TL;DR

InsightSee tackles the challenge of obscured object understanding in visual scenes by introducing a multi-agent framework comprising a description agent, two reasoning agents, and a decision agent that interact through adversarial reasoning. Using GPT-4V as the base VLM, the system demonstrates strong spatial understanding across nine SEED-Bench dimensions, achieving state-of-the-art performance on six tasks and notable gains in attributes, location, counting, and visual reasoning. A qualitative case study highlights the benefits of collaborative description and adversarial refinement in resolving ambiguities. The work offers a generalizable, iterative approach to enhance VLMs for complex visual interpretation with potential applications in autonomous systems and robotics, while noting limitations in text recognition and proposing OCR integration as future work.

Abstract

Accurate visual understanding is imperative for advancing autonomous systems and intelligent robots. Despite the powerful capabilities of vision-language models (VLMs) in processing complex visual scenes, precisely recognizing obscured or ambiguously presented visual elements remains challenging. To tackle such issues, this paper proposes InsightSee, a multi-agent framework to enhance VLMs' interpretative capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The design of these agents and the mechanisms by which they can be enhanced in visual information processing are presented. Experimental results demonstrate that the InsightSee framework not only boosts performance on specific visual tasks but also retains the original models' strength. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.

InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding

TL;DR

InsightSee tackles the challenge of obscured object understanding in visual scenes by introducing a multi-agent framework comprising a description agent, two reasoning agents, and a decision agent that interact through adversarial reasoning. Using GPT-4V as the base VLM, the system demonstrates strong spatial understanding across nine SEED-Bench dimensions, achieving state-of-the-art performance on six tasks and notable gains in attributes, location, counting, and visual reasoning. A qualitative case study highlights the benefits of collaborative description and adversarial refinement in resolving ambiguities. The work offers a generalizable, iterative approach to enhance VLMs for complex visual interpretation with potential applications in autonomous systems and robotics, while noting limitations in text recognition and proposing OCR integration as future work.

Abstract

Accurate visual understanding is imperative for advancing autonomous systems and intelligent robots. Despite the powerful capabilities of vision-language models (VLMs) in processing complex visual scenes, precisely recognizing obscured or ambiguously presented visual elements remains challenging. To tackle such issues, this paper proposes InsightSee, a multi-agent framework to enhance VLMs' interpretative capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The design of these agents and the mechanisms by which they can be enhanced in visual information processing are presented. Experimental results demonstrate that the InsightSee framework not only boosts performance on specific visual tasks but also retains the original models' strength. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.
Paper Structure (14 sections, 5 figures, 1 table)

This paper contains 14 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Framework of the Proposed InsightSee
  4. Description Agent
  5. Reasoning Agent
  6. Decision Agent
  7. Experiments
  8. Datasets
  9. Baseline Methods
  10. Implementation Details
  11. Results
  12. Experimental Results
  13. Case Study
  14. Conclusion

Figures (5)

  • Figure 1: Adding a reasoning process can enhance the VLM’s ability to resolve complex visual tasks.
  • Figure 2: Overview of the InsightSee
  • Figure 3: Distribution of the dataset
  • Figure 4: Real experimental scenarios
  • Figure 5: Examples of which GPT-4V fails while InsightSee succeeds.