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GroundingGPT:Language Enhanced Multi-modal Grounding Model (2401.06071v5)

Published 11 Jan 2024 in cs.CV and cs.CL
GroundingGPT:Language Enhanced Multi-modal Grounding Model

Abstract: Multi-modal LLMs have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize capturing global information within each modality while neglecting the importance of perceiving local information across modalities. Consequently, these models lack the ability to effectively understand the fine-grained details of input data, limiting their performance in tasks that require a more nuanced understanding. To address this limitation, there is a compelling need to develop models that enable fine-grained understanding across multiple modalities, thereby enhancing their applicability to a wide range of tasks. In this paper, we propose GroundingGPT, a language enhanced multi-modal grounding model. Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input. It demonstrates precise identification and localization of specific regions in images or moments in videos. To achieve this objective, we design a diversified dataset construction pipeline, resulting in a multi-modal, multi-granularity dataset for model training. The code, dataset, and demo of our model can be found at https: //github.com/lzw-lzw/GroundingGPT.

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Overview of GroundingGPT: Language Enhanced Multi-modal Grounding Model

The paper presents GroundingGPT, a model designed to enhance fine-grained grounding tasks across multiple modalities (image, video, and audio) by leveraging advancements in Multi-modal LLMs (MLLMs). Unlike existing MLLMs, which primarily focus on capturing global information, GroundingGPT is developed to address the gap in understanding local, fine-grained details essential for grounding tasks.

Model Architecture and Approach

GroundingGPT adopts an end-to-end architecture featuring modality-specific adapters that align features from image, video, and audio encoders with the embedding space of LLMs. The model's unique contribution lies in its fine-grained understanding capability, facilitated by a three-stage coarse-to-fine training strategy:

  1. Multi-modal Pre-training: This stage establishes the model's high-level semantic understanding using coarse-grained multimodal data.
  2. Fine-grained Alignment Tuning: The model then undergoes training to capture detailed information such as spatial coordinates within images and temporal sequences in videos. This stage addresses the scarcity of data through a specifically constructed multi-modal dataset that enhances the model’s grounding and understanding capabilities.
  3. Multi-granularity Instruction Tuning: Finally, nuanced instruction tuning is applied to refine the model's responses and improve its multi-modal interactions. This stage utilizes a diverse array of instruction-tuning datasets to ensure robust fine-grained understanding across different modalities.

Comparative Analysis and Results

GroundingGPT is compared against other MLLMs across multiple benchmarks:

  • In image grounding tasks, such as the referring expression comprehension (REC) task on datasets like RefCOCO and RefCOCO+, GroundingGPT exhibits superior performance compared to models like Shikra and Ferret, both of which leverage additional modules for image perception.
  • For video grounding, GroundingGPT significantly outperforms other baseline models on temporal grounding tasks, indicating its advanced temporal localization capabilities.
  • Across a spectrum of visual question-answering and image understanding benchmarks, including VQA-v2 and TextVQA, GroundingGPT consistently achieves high metrics, demonstrating substantial improvements in interpreting complex visual scenarios.
  • The paper also highlights GroundingGPT’s ability to mitigate object hallucination, presenting results on the POPE benchmark that underscore its effective integration of fine-grained information to reduce false positives in image descriptions.

Implications and Future Directions

The introduction of a unified approach to multi-modal grounding and understanding embodied by GroundingGPT has several important implications:

  • Practical Applications: The enhanced grounding capability can be leveraged in areas requiring precise spatial or temporal understanding, such as autonomous systems, video surveillance, and human-computer interaction technologies.
  • Theoretical Advancements: GroundingGPT highlights potential directions for further exploration in multi-modal research, particularly in balancing global and local data integration across varying input types.
  • Further Research: Speculative avenues include refining the sampling strategy for video and audio inputs to minimize information loss and exploring additional cross-modal applications where multiple input modalities are processed simultaneously. Moreover, expanding grounding tasks to include outputs such as segmentation masks may add further utility.

Overall, this paper contributes significantly to the field by addressing critical limitations in existing MLLMs and offering a comprehensive solution that enhances multi-modal interaction capabilities, setting a benchmark for future explorations in AI-grounding tasks.

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References (37)
  1. Localizing moments in video with natural language. In Proceedings of the IEEE international conference on computer vision, pages 5803–5812.
  2. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901.
  3. X-llm: Bootstrapping advanced large language models by treating multi-modalities as foreign languages. arXiv preprint arXiv:2305.04160.
  4. Localizing visual sounds the hard way. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16867–16876.
  5. Shikra: Unleashing multimodal llm’s referential dialogue magic. arXiv preprint arXiv:2306.15195.
  6. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104–120. Springer.
  7. Clotho: An audio captioning dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 736–740. IEEE.
  8. Tall: Temporal activity localization via language query. In Proceedings of the IEEE international conference on computer vision, pages 5267–5275.
  9. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180–15190.
  10. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780–1790.
  11. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 787–798.
  12. Dense-captioning events in videos. In Proceedings of the IEEE international conference on computer vision, pages 706–715.
  13. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123:32–73.
  14. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597.
  15. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355.
  16. Visual instruction tuning. arXiv preprint arXiv:2304.08485.
  17. Valley: Video assistant with large language model enhanced ability. arXiv preprint arXiv:2306.07207.
  18. Generation and comprehension of unambiguous object descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 11–20.
  19. Wavcaps: A chatgpt-assisted weakly-labelled audio captioning dataset for audio-language multimodal research. arXiv preprint arXiv:2303.17395.
  20. OpenAI. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  21. Kosmos-2: Grounding multimodal large language models to the world. arXiv preprint arXiv:2306.14824.
  22. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Proceedings of the IEEE international conference on computer vision, pages 2641–2649.
  23. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR.
  24. Llasm: Large language and speech model. arXiv preprint arXiv:2308.15930.
  25. Pandagpt: One model to instruction-follow them all. arXiv preprint arXiv:2305.16355.
  26. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  27. Next-gpt: Any-to-any multimodal llm. arXiv preprint arXiv:2309.05519.
  28. Unitab: Unifying text and box outputs for grounded vision-language modeling. In European Conference on Computer Vision, pages 521–539. Springer.
  29. mplug-owl: Modularization empowers large language models with multimodality.
  30. Ferret: Refer and ground anything anywhere at any granularity. arXiv preprint arXiv:2310.07704.
  31. From recognition to cognition: Visual commonsense reasoning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6720–6731.
  32. Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414.
  33. Next-chat: An lmm for chat, detection and segmentation. arXiv preprint arXiv:2311.04498.
  34. Speechgpt: Empowering large language models with intrinsic cross-modal conversational abilities. arXiv preprint arXiv:2305.11000.
  35. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858.
  36. Bubogpt: Enabling visual grounding in multi-modal llms. arXiv preprint arXiv:2307.08581.
  37. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592.
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Authors (12)
  1. Zhaowei Li (13 papers)
  2. Qi Xu (66 papers)
  3. Dong Zhang (169 papers)
  4. Hang Song (18 papers)
  5. Yiqing Cai (6 papers)
  6. Qi Qi (66 papers)
  7. Ran Zhou (35 papers)
  8. Junting Pan (30 papers)
  9. Zefeng Li (31 papers)
  10. Van Tu Vu (1 paper)
  11. Zhida Huang (6 papers)
  12. Tao Wang (700 papers)
Citations (18)
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GitHub

  1. GitHub - lzw-lzw/LEGO: LEGO:Language-Enhanced Multi-modal Grounding Model (245 stars)