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Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs...
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especially in 3D semantic segmentation. However, sparse CNNs are still valuab...
Understanding Convolutional Neural Networks for Text Classification
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a myster...
Automatically designing CNN architectures using genetic algorithm for image classification
Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For most state-of-the-art CNNs, their architectures are...
Scaling Spherical CNNs
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), wh...
adobe/antialiased-cnns
pip install antialiased-cnns to improve stability and accuracy (⭐ 1681)
Cluster-CAM: Cluster-Weighted Visual Interpretation of CNNs' Decision in Image Classificat…
Despite the tremendous success of convolutional neural networks (CNNs) in computer vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation mapping (CAM), a famous visualization technique to interpret CNN's decision...
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been p...
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
Recently, convolutional neural networks (CNNs) with large size kernels have attracted much attention in the computer vision field, following the success of the Vision Transformers. Large kernel CNNs have been reported to perform well in downstream vi...
Testing predictions of representation cost theory with CNNs
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of sensitivity to signals of different frequency. In particular, a number of empirical studies have documented CNNs sensitivity to low-frequency signals...
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs...
CLAD: A Contrastive Learning based Approach for Background Debiasing
Convolutional neural networks (CNNs) have achieved superhuman performance in multiple vision tasks, especially image classification. However, unlike humans, CNNs leverage spurious features, such as background information to make decisions. This tende...
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have establi...
Chinese Herbal Recognition based on Competitive Attentional Fusion of Multi-hierarchies Py…
Convolution neural netwotks (CNNs) are successfully applied in image recognition task. In this study, we explore the approach of automatic herbal recognition with CNNs and build the standard Chinese herbs datasets firstly. According to the characteri...
Multiple VLAD encoding of CNNs for image classification
Despite the effectiveness of convolutional neural networks (CNNs) especially in image classification tasks, the effect of convolution features on learned representations is still limited. It mostly focuses on the salient object of the images, but ign...
Controlled-rearing studies of newborn chicks and deep neural networks
Convolutional neural networks (CNNs) can now achieve human-level performance on challenging object recognition tasks. CNNs are also the leading quantitative models in terms of predicting neural and behavioral responses in visual recognition tasks. Ho...
CNNs: Convolutional Neural Networks - AI Vision Guide
Nov 18, 2025 · Learn about Convolutional Neural Networks (CNNs), the core of AI vision. This ultimate guide covers CNN layers, applications, and how to build them.