Large multimodal models like LLaVA, MiniGPT4, mPLUG-Owl, and Qwen-VL have made rapid progress in handling and analyzing various types of data. However, there are obstacles to overcome, such as dealing with complex scenarios and the need for higher-quality training data. In response, researchers from Huazhong University of Science and Technology and Kingsoft have developed a resource-efficient technique called Monkey, which leverages pre-existing models to increase input resolution. Monkey uses a sliding window approach to divide high-resolution pictures and encode each patch individually for improved image understanding. Monkey has shown promising results in tasks like image captioning and visual question answering.
Introducing ‘Monkey’: Enhancing Input Resolution and Contextual Association in Large Multimodal Models
Large multimodal models are gaining popularity in handling and analyzing diverse data types such as text and pictures. Innovative models like LLaVA, MiniGPT4, mPLUG-Owl, and Qwen-VL have shown remarkable progress in this field. However, there are challenges in dealing with complex scenarios due to varying picture resolutions and the need for high-quality training data. To address these issues, researchers from Huazhong University of Science and Technology and Kingsoft have developed a resource-efficient technique called Monkey.
Monkey leverages pre-existing large multimodal models to increase input resolution without the time-consuming pretraining process. It uses a sliding window approach to divide high-resolution pictures into manageable portions. Each patch is individually encoded by a static visual encoder, multiple LoRA modifications, and a trainable visual resampler. The language decoder is then provided with these patch encodings and the global picture encoding for improved image understanding.
This approach has several practical benefits:
1. Associations within Context
The research team has implemented a multi-level strategy to improve the model’s ability to comprehend relationships between different targets and explore common knowledge when generating text descriptions. This results in more insightful and thorough findings.
2. Enhanced Resolution
Monkey supports resolutions up to 1344 x 896, surpassing the typical 448 x 448 resolution used in large multimodal models. This higher resolution enables the model to identify and understand small or densely packed objects and text.
3. Performance Improvements
The Monkey model has been tested on 16 different datasets and has shown competitive performance in tasks such as Image Captioning, General Visual Question Answering, Scene Text-centric Visual Question Answering, and Document-oriented Visual Question Answering.
To learn more about Monkey, you can check out the research paper and the corresponding Github repository. All credit for this research goes to the project researchers.
If you are interested in evolving your company with AI and staying competitive, consider exploring how Monkey can enhance input resolution and contextual association in your large multimodal models. AI can redefine your work processes and provide automation opportunities. Connect with us at hello@itinai.com for AI KPI management advice, and stay tuned on our Telegram (t.me/itinainews) or Twitter (@itinaicom) for continuous insights into leveraging AI.
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