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Honeybee: Locality-enhanced Projector for Multimodal LLM (2312.06742v2)

Published 11 Dec 2023 in cs.CV, cs.AI, cs.CL, and cs.LG
Honeybee: Locality-enhanced Projector for Multimodal LLM

Abstract: In Multimodal LLMs (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' overall efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://github.com/kakaobrain/honeybee.

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Honeybee: Locality-enhanced Projector for Multimodal LLM

The paper "Honeybee: Locality-enhanced Projector for Multimodal LLM" addresses a critical yet underexplored component in Multimodal LLMs (MLLMs)—the visual projector. A visual projector is vital for translating visual features from a vision encoder into a format that an LLM can understand. This paper identifies two key properties essential for an effective visual projector: flexibility in the number of visual tokens and the ability to preserve the local context. Based on these insights, the authors propose novel locally-enhanced projector designs, namely "Honeybee," which incorporates convolution and deformable attention mechanisms to satisfy these properties.

Key Contributions and Findings

  1. Identification of Essential Projector Properties:

The paper begins by identifying two essential properties for the effectiveness of visual projectors: - Flexibility in determining the number of visual tokens, which is crucial for the computational efficiency of MLLMs. - Preservation of local context from visual features, which aids in better spatial understanding.

  1. Proposal of Locality-enhanced Projectors:

The authors introduce two novel projector designs: - C-Abstractor: Utilizes convolution operations to maintain local context. - D-Abstractor: Employs deformable attention to dynamically adjust its focus, preserving local contexts comprehensively.

These designs aim to balance the trade-off between maintaining spatial details and ensuring computational efficiency.

  1. Extensive Evaluation and Benchmarking:

Honeybee's performance was rigorously evaluated against various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench. - Honeybee showcased superior performance across these benchmarks compared to state-of-the-art methods. - Notably, Honeybee outperformed previous methods significantly in tasks requiring fine-grained spatial understanding, attributed to its locality-enhanced projector.

  1. Comprehensive Training and Instruction Tuning: A pivotal part of the paper is the examination of training methods using multifaceted instruction datasets. The authors present strategies to effectively harness diverse visual instruction data, crucial for improving the robustness and capabilities of MLLMs.
  2. Hidden Recipe for Effective Training: Delving into the specifics, the paper outlines several subtle design choices, including dataset balancing, template granularity and diversity, and the utility of multi-turn examples with de-duplication. These aspects collectively contribute to the efficient training of MLLMs.

Numerical Results

The Honeybee models exhibit significant improvements in several benchmarks: - On MMBench, Honeybee-7B with C-Abstractor achieved an accuracy of 70.1, outperforming previous models like LLaVA-1.5. - Honeybee-13B with C-Abstractor achieved 77.5 in , and N=1730, which are notable jumps in performance over existing models.

Practical and Theoretical Implications

The introduction of locality-enhanced projectors presents substantial implications for the development of MLLMs: - Efficiency Gains: The flexibility in managing visual tokens translates directly to improved computational efficiency, allowing larger models to be deployed in resource-constrained environments. - Improved Understanding: Enhanced local context preservation significantly improves the model's capability to understand and reason about spatial relationships in visual data, pushing the envelope in tasks like visual question answering and scene understanding.

Speculations on Future Developments

Looking forward, the methods and insights from this paper can catalyze several future developments: - Advanced Projector Designs: Employing more sophisticated architectures or hybrid methods combining convolution and deformable attention components could further push the boundaries of visual comprehension in MLLMs. - Unified Multimodal Understanding: Integrating these projectors into a broader array of modalities (e.g., video, 3D data) could lead to more comprehensive and versatile models. - Efficiency in Deployment: The focus on efficiency might inspire more practical MLLM deployments in real-world applications, from autonomous driving to augmented reality systems.

In conclusion, the Honeybee project offers a significant leap in the design and utilization of visual projectors in MLLMs. By meticulously balancing flexibility and locality, it sets a new standard in multimodal understanding while ensuring computational feasibility. The comprehensive strategies for utilizing and instructing datasets further solidify its contributions, offering a potent recipe for training future multimodal systems.

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References (65)
  1. Flamingo: a Visual Language Model for Few-Shot Learning. In NeurIPS, 2022.
  2. Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond. arXiv preprint arXiv:2308.12966, 2023a.
  3. TouchStone: Evaluating Vision-Language Models by Language Models. arXiv preprint arXiv:2308.16890, 2023b.
  4. Language Models are Few-shot Learners. In NeurIPS, 2020.
  5. COYO-700M: Image-Text Pair Dataset. https://github.com/kakaobrain/coyo-dataset, 2022.
  6. MiniGPT-v2: Large Language Model as A Unified Interface for Vision-Language Multi-task Learning. arXiv preprint arXiv:2310.09478, 2023a.
  7. Shikra: Unleashing Multimodal LLM’s Referential Dialogue Magic. arXiv preprint arXiv:2306.15195, 2023b.
  8. Uniter: Universal Image-Text Representation Learning. In ECCV, 2020.
  9. Can Large Language Models Be an Alternative to Human Evaluations? arXiv preprint arXiv:2305.01937, 2023.
  10. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality, 2023.
  11. InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning. arXiv preprint arXiv:2305.06500, 2023.
  12. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning. arXiv preprint arXiv:2307.08691, 2023.
  13. Pengi: An Audio Language Model for Audio Tasks. arXiv preprint arXiv:2305.11834, 2023.
  14. MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models. arXiv preprint arXiv:2306.13394, 2023.
  15. LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model. arXiv preprint arXiv:2304.15010, 2023.
  16. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering. In CVPR, 2017.
  17. 3D-LLM: Injecting the 3D World into Large Language Models. arXiv preprint arXiv:2307.12981, 2023.
  18. LoRA: Low-rank adaptation of large language models. In ICLR, 2022.
  19. Squeeze-and-excitation networks. In CVPR, 2018.
  20. GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering. In CVPR, 2019.
  21. Referitgame: Referring to Objects in Photographs of Natural Scenes. In EMNLP, 2014.
  22. Large Language Models are Temporal and Causal Reasoners for Video Question Answering. In EMNLP, 2023.
  23. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations. IJCV, 2017.
  24. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995, 1995.
  25. Seed-Bench: Benchmarking Multimodal LLMs with Generative Comprehension. arXiv preprint arXiv:2307.16125, 2023a.
  26. BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. In ICML, 2022.
  27. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. In ICML, 2023b.
  28. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023c.
  29. VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation. arXiv preprint arXiv:2106.04632, 2021.
  30. Visual Spatial Reasoning. Transactions of the Association for Computational Linguistics, 2023a.
  31. Aligning Large Multi-Modal Model with Robust Instruction Tuning. arXiv preprint arXiv:2306.14565, 2023b.
  32. Improved baselines with visual instruction tuning. arXiv preprint arXiv:2310.03744, 2023c.
  33. Visual Instruction Tuning. In NeurIPS, 2023d.
  34. MMBench: Is Your Multi-modal Model an All-around Player? arXiv preprint arXiv:2307.06281, 2023e.
  35. G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment. arXiv preprint arXiv:2303.16634, 2023f.
  36. A convnet for the 2020s. In CVPR, 2022.
  37. The flan collection: Designing data and methods for effective instruction tuning. In ICML, 2023.
  38. Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering. In NeurIPS, 2022.
  39. An Empirical Study of Scaling Instruct-tuned Large Multimodal Models. arXiv preprint arXiv:2309.09958, 2023.
  40. Generation and Comprehension of Unambiguous Object Descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
  41. OCR-VQA: Visual Question Answering by Reading Text in Images. In ICDAR, 2019.
  42. OpenAI. ChatGPT, 2023a.
  43. OpenAI. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774, 2023b.
  44. Kosmos-2: Grounding Multimodal Large Language Models to the World. arXiv preprint arXiv:2306.14824, 2023.
  45. Learning Transferable Visual Models From Natural Language Supervision. In ICML, 2021.
  46. Zero: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–16. IEEE, 2020.
  47. A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge. In ECCV, 2022.
  48. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  49. Finetuned language models are zero-shot learners. In ICLR, 2022.
  50. Aggregated residual transformations for deep neural networks. In CVPR, 2017.
  51. PointLLM: Empowering Large Language Models to Understand Point Clouds. arXiv preprint arXiv:2308.16911, 2023.
  52. Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs. arXiv preprint arXiv:2310.00582, 2023.
  53. mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality. arXiv preprint arXiv:2304.14178, 2023.
  54. Ferret: Refer and Ground Anything Anywhere at Any Granularity. arXiv preprint arXiv:2310.07704, 2023.
  55. Coca: Contrastive captioners are image-text foundation models. Transactions on Machine Learning Research, 2022.
  56. Modeling Context in Referring Expressions. In ECCV, 2016.
  57. MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities. arXiv preprint arXiv:2308.02490, 2023.
  58. Florence: A New Foundation Model for Computer Vision. arXiv preprint arXiv:2111.11432, 2021.
  59. What Matters in Training a GPT4-Style Language Model with Multimodal Inputs? arXiv preprint arXiv:2307.02469, 2023.
  60. LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention. arXiv preprint arXiv:2303.16199, 2023a.
  61. LLaVAR: Enhanced Visual Instruction Tuning for Text-rich Image Understanding. arXiv preprint arXiv:2306.17107, 2023b.
  62. Multimodal Chain-of-Thought Reasoning in Language Models. arXiv preprint arXiv:2302.00923, 2023c.
  63. SVIT: Scaling up Visual Instruction Tuning. arXiv preprint arXiv:2307.04087, 2023.
  64. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models. arXiv preprint arXiv:2304.10592, 2023.
  65. Deformable DETR: Deformable Transformers for End-to-End Object Detection. In ICLR, 2021.
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Authors (4)
  1. Junbum Cha (10 papers)
  2. Wooyoung Kang (6 papers)
  3. Jonghwan Mun (16 papers)
  4. Byungseok Roh (16 papers)
Citations (69)
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  1. GitHub - kakaobrain/honeybee: Official implementation of Honeybee (451 stars)
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