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Recent Advancements in Multimodal Large Language Models (MLLM)
Advancements in large language models like ChatGPT have transformed various fields, but their quadratic computation complexity hinders efficiency. Researchers are enhancing MLLMs by integrating multimodal processing capabilities to address this limitation.
Efficient Multimodal Integration with Cobra
Researchers have developed Cobra, a MLLM with linear computational complexity, integrating the efficient Mamba language model into the visual modality. Cobra outperforms current computationally efficient methods and achieves competitive performance in challenging prediction benchmarks. It exhibits significantly faster inference speed than Transformer-based models and is expected to be released as open-source code.
Visual and Language Model Integration
Cobra integrates Mamba’s selective state space model with visual understanding, featuring a vision encoder, a projector, and the Mamba backbone. It aligns visual and textual features and processes the concatenated visual and textual embeddings, demonstrating effectiveness in visual question-answering and spatial reasoning tasks.
Practical AI Solutions
If you want to evolve your company with AI, consider using Cobra for Multimodal Language Learning. It can redefine your work processes, automate customer engagement, and manage interactions across all customer journey stages.
Implementing AI Solutions
To implement AI solutions, start with defining KPIs, selecting the right AI tools, and gradually expanding AI usage. Consider practical AI solutions like the AI Sales Bot designed to automate customer engagement and manage interactions across all customer journey stages.
Connect with Us
For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com or follow our updates on Telegram and Twitter.
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