Beginner Explanation
Imagine you’re watching a movie, and you try to guess what will happen next. If you see a character picking up a phone, you might think they will call someone. Video-next-event prediction is like that! It’s a computer trying to guess what happens next in a video based on what it sees. Just like you use clues from the scene, the computer looks for patterns and actions in the video to make its best guess.Technical Explanation
Video-next-event prediction is a task in machine learning where we train models to predict future actions in a video sequence. This involves analyzing temporal features and understanding the context of events. For instance, we can use convolutional neural networks (CNNs) for spatial feature extraction and recurrent neural networks (RNNs) or transformers for temporal sequence modeling. A simple implementation might involve using a pre-trained CNN to extract features from video frames and then feeding these features into an RNN to predict the next event. Here’s a basic code example: “`python import torch import torch.nn as nn class VideoNextEventPredictor(nn.Module): def __init__(self): super(VideoNextEventPredictor, self).__init__() self.cnn = nn.Sequential( nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2) ) self.rnn = nn.LSTM(16 * 112 * 112, 128, batch_first=True) self.fc = nn.Linear(128, num_classes) def forward(self, x): cnn_out = self.cnn(x) rnn_out, _ = self.rnn(cnn_out.view(cnn_out.size(0), -1, 16 * 112 * 112)) return self.fc(rnn_out[:, -1, :]) “` This model processes video frames and predicts the next event based on learned patterns.Academic Context
Video-next-event prediction is rooted in the fields of computer vision and machine learning, focusing on understanding temporal dynamics in visual data. Key mathematical concepts include convolutional operations for spatial feature extraction and recurrent structures for capturing temporal dependencies. Notable papers include ‘Temporal Segment Networks for Action Recognition in Videos’ (Wang et al., 2016), which introduces methods for understanding video sequences, and ‘3D ResNets for Action Recognition’ (Carreira & Zisserman, 2017), which explores spatio-temporal networks. Ongoing research addresses challenges like multi-modal data integration and real-time prediction accuracy.Code Examples
Example 1:
import torch
import torch.nn as nn
class VideoNextEventPredictor(nn.Module):
def __init__(self):
super(VideoNextEventPredictor, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.rnn = nn.LSTM(16 * 112 * 112, 128, batch_first=True)
self.fc = nn.Linear(128, num_classes)
def forward(self, x):
cnn_out = self.cnn(x)
rnn_out, _ = self.rnn(cnn_out.view(cnn_out.size(0), -1, 16 * 112 * 112))
return self.fc(rnn_out[:, -1, :])Example 2:
def __init__(self):
super(VideoNextEventPredictor, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.rnn = nn.LSTM(16 * 112 * 112, 128, batch_first=True)
self.fc = nn.Linear(128, num_classes)Example 3:
def forward(self, x):
cnn_out = self.cnn(x)
rnn_out, _ = self.rnn(cnn_out.view(cnn_out.size(0), -1, 16 * 112 * 112))
return self.fc(rnn_out[:, -1, :])Example 4:
import torch
import torch.nn as nn
class VideoNextEventPredictor(nn.Module):
def __init__(self):Example 5:
import torch.nn as nn
class VideoNextEventPredictor(nn.Module):
def __init__(self):
super(VideoNextEventPredictor, self).__init__()Example 6:
class VideoNextEventPredictor(nn.Module):
def __init__(self):
super(VideoNextEventPredictor, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),Example 7:
def __init__(self):
super(VideoNextEventPredictor, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
nn.ReLU(),Example 8:
def forward(self, x):
cnn_out = self.cnn(x)
rnn_out, _ = self.rnn(cnn_out.view(cnn_out.size(0), -1, 16 * 112 * 112))
return self.fc(rnn_out[:, -1, :])
```View Source: https://arxiv.org/abs/2511.16669v1