Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
149 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning (2311.07574v2)

Published 13 Nov 2023 in cs.CV

Abstract: Existing visual instruction tuning methods typically prompt LLMs with textual descriptions to generate instruction-following data. Despite the promising performance achieved, these descriptions are derived from image annotations, which are oftentimes coarse-grained. Furthermore, the instructions might even contradict the visual content without observing the entire visual context. To address this challenge, we introduce a fine-grained visual instruction dataset, LVIS-Instruct4V, which contains 220K visually aligned and context-aware instructions produced by prompting the powerful GPT-4V with images from LVIS. Through experimental validation and case studies, we demonstrate that high-quality visual instructional data could improve the performance of LLaVA-1.5, a state-of-the-art large multimodal model, across a wide spectrum of benchmarks by clear margins. Notably, by simply replacing the LLaVA-Instruct with our LVIS-Instruct4V, we achieve better results than LLaVA on most challenging LMM benchmarks, e.g., LLaVA$w$ (76.7 vs. 70.7) and MM-Vet (40.2 vs. 35.4). We release our data and model at https://github.com/X2FD/LVIS-INSTRUCT4V.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (50)
  1. Flamingo: a visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022.
  2. Vqa: Visual question answering. In ICCV, 2015.
  3. Qwen technical report. arXiv preprint arXiv:2309.16609, 2023.
  4. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023.
  5. Language models are few-shot learners. In NeurIPS, 2020.
  6. Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In CVPR, 2021.
  7. Shikra: Unleashing multimodal llm’s referential dialogue magic. arXiv preprint arXiv:2306.15195, 2023.
  8. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
  9. Instructblip: Towards general-purpose vision-language models with instruction tuning. arXiv preprint arXiv:2305.06500, 2023.
  10. PaLM-E: An embodied multimodal language model. arXiv preprint arXiv:2303.03378, 2023.
  11. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023.
  12. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In CVPR, 2017.
  13. Lvis: A dataset for large vocabulary instance segmentation. In CVPR, 2019.
  14. Vizwiz grand challenge: Answering visual questions from blind people. In CVPR, 2018.
  15. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In CVPR, 2019.
  16. IDEFICS. Introducing idefics: An open reproduction of state-of-the-art visual language model. https://huggingface.co/blog/idefics, 2023.
  17. Mdetr-modulated detection for end-to-end multi-modal understanding. In CVPR, 2021.
  18. Referitgame: Referring to objects in photographs of natural scenes. In EMNLP, 2014.
  19. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017.
  20. Lisa: Reasoning segmentation via large language model. arXiv preprint arXiv:2308.00692, 2023.
  21. Multimodal foundation models: From specialists to general-purpose assistants. arXiv preprint arXiv:2309.10020, 2023.
  22. Llava-med: Training a large language-and-vision assistant for biomedicine in one day. arXiv preprint arXiv:2306.00890, 2023.
  23. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.
  24. Improved baselines with visual instruction tuning. arXiv preprint arXiv:2310.03744, 2023.
  25. Visual instruction tuning. In NeurIPS, 2023.
  26. Ok-vqa: A visual question answering benchmark requiring external knowledge. In CVPR, 2019.
  27. Ocr-vqa: Visual question answering by reading text in images. In ICDAR, 2019.
  28. OpenAI. Chatgpt. https://openai.com/blog/chatgpt/, 2023.
  29. OpenAI. Gpt-4 technical report, 2023.
  30. OpenAI. Gpt-4v(ision) system card. https://cdn.openai.com/papers/GPTV_System_Card.pdf, 2023.
  31. Training language models to follow instructions with human feedback. In NeurIPS, 2022.
  32. Kosmos-2: Grounding multimodal large language models to the world. arXiv preprint arXiv:2306.14824, 2023.
  33. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020.
  34. Laion-5b: An open large-scale dataset for training next generation image-text models. In NeurIPS, 2022.
  35. A-okvqa: A benchmark for visual question answering using world knowledge. In ECCV, 2022.
  36. ShareGPT. https://sharegpt.com/, 2023.
  37. Where to look: Focus regions for visual question answering. In CVPR, 2016.
  38. Towards vqa models that can read. In CVPR, 2019.
  39. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  40. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.
  41. Multimodal few-shot learning with frozen language models. In NeurIPS, 2021.
  42. Vicuna. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. https://vicuna.lmsys.org/, 2023.
  43. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560, 2022.
  44. Benchmarking generalization via in-context instructions on 1,600+ language tasks. arXiv preprint arXiv:2204.07705, 2022.
  45. Boosting image captioning with attributes. In ICCV, 2017.
  46. Image captioning with semantic attention. In CVPR, 2016.
  47. Modeling context in referring expressions. In ECCV, 2016.
  48. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023.
  49. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068, 2022.
  50. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023.
Citations (75)
View on Semantic Scholar

Summary

  • The paper introduces LVIS-Instruct4V, a dataset of 220K fine-grained visual instructions generated using GPT-4V to improve multimodal tuning.
  • It leverages the LLaVA-1.5 framework to seamlessly integrate detailed visual annotations with textual context for better model alignment.
  • Experimental results reveal significant improvements, including a 43.6 gain on the MME benchmark, underscoring enhanced visual reasoning capabilities.

An Overview of "To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning"

The paper "To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning" investigates advancements in visual instruction tuning by leveraging the capabilities of GPT-4V, a large multimodal model. The paper addresses the limitations of current visual instruction tuning methods that predominantly rely on textual descriptions derived from coarse-grained image annotations. These methods often lack the nuanced understanding required in visual context alignment, leading to contradictions in instructions relative to visual content.

The authors propose the LVIS-Instruct4V dataset, comprising 220,000 visually fine-grained and context-aware instruction data entries. This dataset leverages GPT-4V for generating instruction-answer pairs by incorporating images directly into the prompting process. By drawing from the LVIS object detection dataset, known for detailed annotations and extensive taxonomy, the paper facilitates a more accurate and contextually rich set of instructions.

Key Contributions and Methodology

  1. Dataset Construction: The paper introduces LVIS-Instruct4V, cultivated using GPT-4V guided by visually contextualized prompts. This collection encompasses 220K instructions, marked by detailed object annotations, enabling finer attention to visual nuances like object positioning, counting, attributes, and interactions.
  2. Architectural Framework: The research employs LLaVA-1.5, a leading large multimodal model, and replaces previous data with the LVIS-Instruct4V dataset. Such an alignment is posited to bridge the gap between visual and textual information more efficiently.
  3. Experimental Outcomes: The incorporation of LVIS-Instruct4V into LLaVA-1.5 delivers notable performance boosts on various benchmarks. Particularly, the improvements are apparent in both traditional QA and modern LMM benchmarks, like VQAv2, GQA, and challenging metrics such as LLaVAw^w and MM-Vet, where the model outperformed existing methods significantly.

Strong Numerical Results

  • With Vicuna-7B, the model achieves a VQAv2 score of 79.2, which improves with a 13B size model to reach 80.1.
  • In particular, the benchmarks reveal a gain of 43.6 on the MME benchmark when scaling LLM components and instruction tuning with LVIS-Instruct4V.

Implications and Future Perspectives

The paper underscores the potential of multimodal models to handle complex visual reasoning tasks more effectively with fine-grained instructions. The authors' approach of leveraging visual context in instruction generation could pioneer future developments in visual AI, expanding the applicability of LLMs in more intricate visual domains.

Future directions could involve expanding the LVIS-Instruct4V with even more diverse data sources, exploring different multimodal architectures, and applying this tuning methodology to real-world applications requiring precise visual-linguistic integration, such as autonomous driving or advanced robotics.

In conclusion, the paper contributes a substantial advancement in visual instruction tuning, demonstrating the pivotal role of contextual visual data over purely language-driven data in enhancing the reasoning capabilities of multimodal models. The LVIS-Instruct4V dataset thus emerges as an instrumental resource in the landscape of computer vision and language interfacing.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - X2FD/LVIS-INSTRUCT4V (128 stars)