Tencent AI Lab and the Chinese University of Hong Kong team collaborated to develop UniRepLKNet, a multi-modal model based on large-core CNN, aiming to challenge the dominance of Transformer in the multi-modal field. The model performs well in processing point clouds, audio and video data, etc. Its advantage is that it can adapt to different data types without changing the model structure. UniRepLKNet has surpassed the Transformer model in benchmark tests such as ImageNet, COCO and ADE20K, proving the huge potential of large-core CNN in multi-modal applications and providing new directions and ideas for multi-modal research.
Tencent AI Lab collaborated with the Hong Kong Chinese team to launch UniRepLKNet to challenge Transformer’s dominance in the multi-modal field. This large-core CNN architecture performs well on point cloud, audio, video and other tasks without changing the model structure. UniRepLKNet surpassed Transformer in tasks such as ImageNet, COCO, and ADE20K, demonstrating the potential of large-core CNN in multi-modal applications.
The success of UniRepLKNet shows that large-core CNN architecture is competitive in multi-modal tasks and provides new ideas for the design of future multi-modal models. Its feature of not needing to change the model structure also simplifies model application and improves development efficiency. This research result has brought new breakthroughs to multi-modal research in the field of artificial intelligence and deserves further attention and in-depth research.