Over the past couple of years, tremendous progress has been made in applying deep learning (DL) techniques to computer vision. Especially, deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance on standard recognition datasets and tasks such as ImageNet Large-Scale ...
2.1. Convolutional Backbones in Computer Vision(当常识自己去看看即可) 2.2. Dense Prediction Tasks(当常识自己去看看即可) 2.3. SelfAttention and Transformer in Vision 3. Pyramid Vision Transformer (PVT) 3.1. Overall Architecture 3.2. Feature Pyramid for Transformer 3.3. Transformer Encoder 3.4. Model...
这里意思是 7 x 7 的卷积层的正则等效于 3 个 3 x 3 的卷积层的叠加。而这样的设计不仅可以大幅...
Computer vision (CV) technologies have been applied extensively in turbid water assessment. However, the accuracies of CV to detect turbidity is limited by several factors such as inferior image quality and adoptation of traditional machine learning (MsL). Several past studies h...
Self-attention layers in Computer Vision take a feature map as input. The goal is to compute attention weights between every pair of features resulting in an updated feature map where each position has information about any other feature within the same image. ...
Network-in-Network 是 Lin 等人提出的,用于增强网络表示能力的一种方法。在它们的模型中,网络中增加了额外的 1×1 1 \times 11×1 的卷积,用以增加网络的深度。我们在我们的网络中重度使用这种方法。然而,在我们的设定中, 1×1 1 \times 11×1 的卷积有着双重目标:最重要的,它们主要被用来作为降维的方法...
In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and resid...
Although convolutional networks (ConvNets) have enjoyed great success in computer vision (CV), it suffers from capturing global information crucial to dense prediction tasks such as object detection and segmentation. In this work, we innovatively propose ConTNet (ConvolutionTransformer Network), combining...
[7] Chi-Chong Wong and Chi-Man Vong. Persistent homology based graph convolution network for fine-grained 3d shape segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 7098–7107, Oct 2021. ...
(CNNs) have become a powerful category of artificial neural networks1. CNNs are commonly used in image recognition to greatly reduce the network complexity and conduct high-precision predictions, with wide applications in object classification, computer vision, real-time translation, and other areas2...