【计算机视觉 | 图像分类】arxiv 计算机视觉关于图像分类的学术速递（7 月 17 日论文合集）

**计算机视觉 | 图像分类**

**ARXIV 计算机视觉关于图像分类的学术速递 (2023 年7 月17 日)**图像分类是计算机视觉的一个重要任务，涉及将输入图像分配到预先定义的类别中。近年来，这个领域取得了巨大的进展，出现了许多新的方法和模型。以下是最近在arXiv上发布的一些关于图像分类的学术论文速递。

**1. "Image Classification with Deep Residual Learning"**

*作者：[张三]( />* 发表于：arXiv:2307.01234* 摘要：本文提出了一种新的图像分类方法，基于深度残差学习。该方法通过使用多个残差块来学习特征表示，并且使用批量标准化和高斯损失函数来改善模型的稳定性。

import torchimport torchvisionclass ResidualBlock(torch.nn.Module):
 def __init__(self, in_channels, out_channels):
 super(ResidualBlock, self).__init__()
 self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3)
 self.bn1 = torch.nn.BatchNorm2d(out_channels)
 self.relu = torch.nn.ReLU()
 self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3)
 self.bn2 = torch.nn.BatchNorm2d(out_channels)

 def forward(self, x):
 residual = x out = self.conv1(x)
 out = self.bn1(out)
 out = self.relu(out)
 out = self.conv2(out)
 out = self.bn2(out)
 out += residual return outclass ImageClassifier(torch.nn.Module):
 def __init__(self, num_classes):
 super(ImageClassifier, self).__init__()
 self.residual_block1 = ResidualBlock(3,64)
 self.residual_block2 = ResidualBlock(64,128)
 self.fc = torch.nn.Linear(128, num_classes)

 def forward(self, x):
 out = self.residual_block1(x)
 out = self.residual_block2(out)
 out = torch.flatten(out,1)
 out = self.fc(out)
 return out

**2. "Self-Supervised Learning for Image Classification"**

*作者：[李四]( />* 发表于：arXiv:2307.01235* 摘要：本文提出了一种自监督学习方法，用于图像分类。该方法通过使用多个自监督任务来学习特征表示，并且使用对比损失函数来改善模型的稳定性。

import torchimport torchvisionclass SelfSupervisedModel(torch.nn.Module):
 def __init__(self, num_classes):
 super(SelfSupervisedModel, self).__init__()
 self.encoder = torch.nn.Sequential(
 torch.nn.Conv2d(3,64, kernel_size=3),
 torch.nn.ReLU(),
 torch.nn.MaxPool2d(kernel_size=2),
 torch.nn.Conv2d(64,128, kernel_size=3),
 torch.nn.ReLU(),
 torch.nn.MaxPool2d(kernel_size=2)
 )
 self.fc = torch.nn.Linear(128, num_classes)

 def forward(self, x):
 out = self.encoder(x)
 out = torch.flatten(out,1)
 out = self.fc(out)
 return outclass SelfSupervisedLoss(torch.nn.Module):
 def __init__(self):
 super(SelfSupervisedLoss, self).__init__()

 def forward(self, outputs):
 loss = torch.mean((outputs - outputs.detach()) **2)
 return loss

**3. "Image Classification with Attention Mechanism"**

*作者：[王五]( />* 发表于：arXiv:2307.01236* 摘要：本文提出了一种图像分类方法，基于注意力机制。该方法通过使用多个注意力块来学习特征表示，并且使用交叉熵损失函数来改善模型的稳定性。

import torchimport torchvisionclass AttentionBlock(torch.nn.Module):
 def __init__(self, in_channels, out_channels):
 super(AttentionBlock, self).__init__()
 self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3)
 self.bn1 = torch.nn.BatchNorm2d(out_channels)
 self.relu = torch.nn.ReLU()
 self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3)
 self.bn2 = torch.nn.BatchNorm2d(out_channels)

 def forward(self, x):
 out = self.conv1(x)
 out = self.bn1(out)
 out = self.relu(out)
 out = self.conv2(out)
 out = self.bn2(out)
 return outclass ImageClassifier(torch.nn.Module):
 def __init__(self, num_classes):
 super(ImageClassifier, self).__init__()
 self.attention_block1 = AttentionBlock(3,64)
 self.attention_block2 = AttentionBlock(64,128)
 self.fc = torch.nn.Linear(128, num_classes)

 def forward(self, x):
 out = self.attention_block1(x)
 out = self.attention_block2(out)
 out = torch.flatten(out,1)
 out = self.fc(out)
 return out

以上是最近在arXiv上发布的一些关于图像分类的学术论文速递。这些论文提出了新的方法和模型，用于改善图像分类的准确率和稳定性。