Kolmogorov-Arnold Networks (KANs) introduce a paradigm of neural modeling
that implements learnable functions on the edges of the networks, diverging
from the traditional node-centric activations in neural networks. This work
assesses the applicability and efficacy of KANs in visual modeling, focusing on
the image recognition task. We mainly analyze the performance and efficiency of
different network architectures built using KAN concepts along with
conventional building blocks of convolutional and linear layers, enabling a
comparative analysis with the conventional models. Our findings are aimed at
contributing to understanding the potential of KANs in computer vision,
highlighting both their strengths and areas for further research. Our
evaluation shows that whereas KAN-based architectures perform in-line with the
original claims of KAN paper for performance and model-complexity in the case
of simpler vision datasets like MNIST, the advantages seem to diminish even for
slightly more complex datasets like CIFAR-10.

Kolmogorov-Arnold Networks (KANs) are evaluated for visual modeling in image recognition tasks, comparing their performance and efficiency with conventional models, highlighting their potential and areas for further research.

KAN 在计算机视觉中的适用性：初步调查

Suitability of KANs for Computer Vision: A preliminary investigation

Recent progress in vision Transformers exhibits great success in various
tasks driven by the new spatial modeling mechanism based on dot-product
self-attention. In this paper, we show that the key ingredients behind the
vision Transformers, namely input-adaptive, long-range and high-order spatial
interactions, can also be efficiently implemented with a convolution-based
framework. We present the Recursive Gated Convolution
($\textit{g}^\textit{n}$Conv) that performs high-order spatial interactions
with gated convolutions and recursive designs. The new operation is highly
flexible and customizable, which is compatible with various variants of
convolution and extends the two-order interactions in self-attention to
arbitrary orders without introducing significant extra computation.
$\textit{g}^\textit{n}$Conv can serve as a plug-and-play module to improve
various vision Transformers and convolution-based models. Based on the
operation, we construct a new family of generic vision backbones named HorNet.
Extensive experiments on ImageNet classification, COCO object detection and
ADE20K semantic segmentation show HorNet outperform Swin Transformers and
ConvNeXt by a significant margin with similar overall architecture and training
configurations. HorNet also shows favorable scalability to more training data
and larger model sizes. Apart from the effectiveness in visual encoders, we
also show $\textit{g}^\textit{n}$Conv can be applied to task-specific decoders
and consistently improve dense prediction performance with less computation.
Our results demonstrate that $\textit{g}^\textit{n}$Conv can be a new basic
module for visual modeling that effectively combines the merits of both vision
Transformers and CNNs. Code is available at
this https URL

本文提出一种名为 'Recursive Gated Convolution' 的新的可编程高效的卷积操作，它结合了 Vision Transformers 和 CNNs 的优点并能够优化视觉数据的编码和解码，引入这个操作到 HorNet 中能够提高大规模视觉识别、目标检测和图像分割任务的性能。

HorNet: 递归门控卷积实现高阶空间交互效率化

HorNet: Efficient High-Order Spatial Interactions with Recursive Gated  Convolutions

Non-Local Attention (NLA) brings significant improvement for Single Image
Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural
images. However, NLA gives noisy information large weights and consumes
quadratic computation resources with respect to the input size, limiting its
performance and application. In this paper, we propose a novel Efficient
Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling
and leverage more relevant non-local features. Specifically, ENLCA consists of
two parts, Efficient Non-Local Attention (ENLA) and Sparse Aggregation. ENLA
adopts the kernel method to approximate exponential function and obtains linear
computation complexity. For Sparse Aggregation, we multiply inputs by an
amplification factor to focus on informative features, yet the variance of
approximation increases exponentially. Therefore, contrastive learning is
applied to further separate relevant and irrelevant features. To demonstrate
the effectiveness of ENLCA, we build an architecture called Efficient Non-Local
Contrastive Network (ENLCN) by adding a few of our modules in a simple
backbone. Extensive experimental results show that ENLCN reaches superior
performance over state-of-the-art approaches on both quantitative and
qualitative evaluations.

本文提出了一种新的高效非局部对比度注意力 (Efficient Non-Local Contrastive Attention, ENLCA)，实现了长距离视觉建模和更多相关非局部特征的利用。通过引入核方法来近似指数函数，达到线性计算复杂度，并通过对比学习从而进一步分离相关和无关特征，证明了 ENLCA 的有效性。