The integration of Multi-layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and attention mechanisms has ushered in enhanced performance across various computer vision tasks. A novel lightweight neural network architecture named FasterMLP has been proposed to achieve remarkable computational efficiency and accuracy, particularly within resource-constrained and real-time applications.
FasterMLP is ingeniously structured to amalgamate the local connectivity and weight-sharing properties of CNNs with the global feature representation capabilities of MLPs. Its innovative feature extraction capabilities are boosted by the Convolutional Block Attention Module (CBAM), and spatial dimensions are effectively diminished using Haar wavelet downsampling without sacrificing significant feature information.
The architecture, streamlined across four distinct stages, has been stringently evaluated on multiple benchmarks. On the ImageNet-1K dataset, FasterMLP-S outperformed MobileViT-XXS with top-1 accuracy exceeding it by 3.9%, all the whilst executing twice as fast on GPU and 2.7 times faster on CPU. Complementarily on the COCO dataset, FasterMLP-L’s performance was comparable to FasterNet-L yet achieved this with appreciably fewer parameters, and on the Cityscapes dataset, it secured an impressive mean Intersection-over-Union score of 81.7%, surpassing existing models like CCNet and DANet.
These compelling results cumulatively demonstrate how FasterMLP efficiently balances computational efficiency and accuracy, positioning it as highly suitable for visual perception tasks within resource-constrained settings—including autonomous driving.
Recent advancements have witnessed Vision Transformers (ViTs) gaining acclaim due to their exceptional performance; nevertheless, the growing model sizes also precipitate significant computational costs. Applications necessitating real-time responsiveness—like autonomous driving—often find these heavy computational demands untenable. Although existing studies attempt to mitigate model parameters or computational overhead (such as FLOPs), they often neglect imperative metrics such as inference speed. This becomes pivotal as traditional MLP networks, albeit optimized for feature representation, are limited by inefficiencies owing to dense connectivity, particularly when handling large-scale data.
The groundwork for FasterMLP’s innovation rests within its combination of the local connectivity framework seen within CNNs and the strong feature representation characteristic of MLPs, thereby achieving substantial reductions in computational costs and showcasing enhanced inference speed on experimental evaluation. This dual advantage indicates its rigid potential for real-time applications whilst retaining accuracy.
The innovative integration of convolutional modules, CBAM, and Haar wavelet downsampling codifies this hybrid architecture; effectively enabling precise feature extraction even within real-time applications. The resultant experiments signify the competitive prowess of FasterMLP across principal computer vision tasks including image classification, object detection, and even semantic segmentation—all supporting its utility for real-time scenarios.
Modeling just like this achieves dual objectives—steep accuracy with markedly lower operational cost. FasterMLP surpasses the silhouette cast by numerous lightweight architectures, boasting both efficiency and accuracy.
Future enhancements could pursue lightweight global attention mechanisms or streamline the Haar wavelet module, which would lead to enhanced representation without sacrificing efficiency. Such adaptations will allow potential findings to be applicable to alternative domains including medical imaging and remote sensing.
FasterMLP, as it stands, offers significant promise for translating advanced neural network models onto platforms where rapid, real-time image processing is indispensable.