Researchers from the Chinese University of Hong Kong and Tencent AI Lab Propose a Multimodal Pathway to Improve Transformers with Irrelevant Data from Other Modalities

Researchers from the Chinese University of Hong Kong and Tencent AI Lab Propose a Multimodal Pathway to Improve Transformers with Irrelevant Data from Other Modalities AI News, AI, AI tools, Innovation, itinai.com, LLM, MarkTechPost, Mohammad Asjad, t.me/itinai 🚀 **Transformers in AI Applications** 🚀 Transformers have revolutionized various tasks in AI, from text classification to audio spectrogram recognition. Now, researchers at The Chinese University of Hong Kong and Tencent AI Lab have introduced the Multimodal Pathway Transformer (M2PT) to take transformer performance to the next level by incorporating irrelevant data from other modalities. This breakthrough has led to significant performance improvements across image, point cloud, video, and audio recognition tasks. 🔍 **Practical Solutions and Value** 🔍 The M2PT enhances transformers designed for specific modalities, such as ImageNet, by integrating unrelated data from audio or point cloud datasets. The result? Consistent and substantial performance improvements across various recognition tasks. This means your AI systems can achieve better accuracy and task performance, giving your company a competitive edge. 🌐 **Multimodal Pathway Transformer (M2PT)** 🌐 M2PT connects components of a target modality model with an auxiliary model through pathways, allowing the utilization of the transformer’s capabilities from two modalities. This approach involves modality-specific tokenization and task-specific heads, as well as the incorporation of auxiliary model transformer blocks using cross-module re-parameterization, all without incurring inference costs. 📊 **Experimental Findings** 📊 Experimental results using the ViT-B architecture across models show that M2PT-Video, M2PT-Audio, and M2PT-Point outperform baseline models in image recognition tasks. Notably, M2PT-Point showcases substantial enhancements in metrics like APbox, APmask, and mIOU compared to baseline models, demonstrating its effectiveness across various recognition tasks. 🔗 **AI Solutions for Middle Managers** 🔗 To keep your company competitive and harness the power of AI, consider leveraging the Multimodal Pathway Transformer. By incorporating irrelevant data from other modalities, you can improve transformer performance and redefine your way of work. For practical AI solutions, explore the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all customer journey stages. 📈 **Practical AI Solution** 📈 For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com and stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom. 🔗 **Useful Links** 🔗 - AI Lab in Telegram @aiscrumbot – free consultation - Researchers from the Chinese University of Hong Kong and Tencent AI Lab Propose a Multimodal Pathway to Improve Transformers with Irrelevant Data from Other Modalities - MarkTechPost - Twitter – @itinaicom