This AI Paper from China Introduces UniRepLKNet: Pioneering Large-Kernel ConvNet Architectures for Enhanced Cross-Modal Performance in Image, Audio, and Time-Series Data Analysis

CNNs (Convolutional neural networks) have become a popular technique for image recognition in recent years. They have been highly successful in object detection, classification, and segmentation tasks. However, new challenges have emerged as these networks have grown more complex. Researchers from Tencent AI Lab and The Chinese University of Hong Kong have proposed four guidelines to address the architectural challenges in large-kernel CNNs. These guidelines aim to improve image recognition by extending the applications of large kernels beyond vision tasks, such as time-series forecasting and audio recognition.

UniRepLKNet explores the efficacy of ConvNets with very large kernels, extending beyond spatial convolution to domains like point cloud data, time-series forecasting, audio, and video recognition. While previous works introduced large seeds differently, UniRepLKNet focuses on architectural design for ConvNets with such kernels. It outperforms specialized models in 3D pattern learning, time-series forecasting, and audio recognition. Despite slightly lower video recognition accuracy than technical models, UniRepLKNet is a generalist model trained from scratch, providing versatility across domains.

UniRepLKNet introduces architectural guidelines for ConvNets with large kernels, emphasizing wide coverage without excessive depth. The guidelines address the limitations of Vision Transformers (ViTs), focus on efficient structures, re-parameterizing conv layers, task-based kernel sizing, and incorporating 3×3 conv layers. UniRepLKNet outperforms existing large-kernel ConvNets and recent architectures in image recognition, showcasing its efficiency and accuracy. It demonstrates universal perception abilities in tasks beyond vision, excelling in time-series forecasting and audio recognition. UniRepLKNet exhibits versatility in learning 3D patterns in point cloud data, surpassing specialized ConvNet models.

The study introduces four architectural guidelines for large-kernel ConvNets, emphasizing the distinctive features of large kernels. UniRepLKNet follows these guidelines, leveraging large seeds to outperform competitors in image recognition. It showcases universal perception abilities, excelling in time-series forecasting and audio recognition without modality-specific customization. UniRepLKNet also proves versatile in learning 3D patterns in point cloud data, surpassing specialized ConvNet models. Dilated Reparam Block is introduced to enhance non-dilated large-kernel conv layers. UniRepLKNet’s architecture combines large kernels with dilated conv layers, capturing small-scale and sparse patterns for improved feature quality.

UniRepLKNet’s architecture achieves top-tier performance in image recognition tasks, boasting an ImageNet accuracy of 88.0%, ADE20K mIoU of 55.6%, and COCO box AP of 56.4%. Its universal perception ability is evident in leading performance in time-series forecasting and audio recognition, outperforming competitors in MSE and MAE in the Global Temperature and Wind Speed Forecasting challenge. UniRepLKNet excels in learning 3D patterns in point cloud data, surpassing specialized ConvNet models. The model showcases promising results in downstream tasks like semantic segmentation, affirming its superior performance and efficiency across diverse domains.

In conclusion, the research takeaways can be expressed below points:

• The research introduces four architectural guidelines for large-kernel ConvNets
• These guidelines emphasize the unique characteristics of large-kernel ConvNets
• UniRepLKNet, a ConvNet model designed following these guidelines, outperforms its competitors in image recognition tasks.
• UniRepLKNet showcases universal perception ability, excelling in time-series forecasting and audio recognition without customization.
• UniRepLKNet is versatile in learning 3D patterns in point cloud data, surpassing specialized models.
• The study introduces the Dilated Reparam Block, which enhances the performance of large-kernel conv layers.
• The research contributes valuable architectural guidelines, introduces UniRepLKNet and its capabilities, and presents the Dilated Reparam Block concept.

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