Beginner Explanation
Imagine you’re watching a series of short videos, and you have to guess what happens next based on what you’ve seen so far. VANS-Data-100K is like a giant library of these short videos where each video has a story. Researchers use this library to teach computers how to predict what happens next in a video, just like you would try to guess the next scene in your favorite movie. By practicing on this dataset, computers can get better at understanding and predicting actions in videos, which is super helpful for things like making smarter robots or improving video recommendations.Technical Explanation
VANS-Data-100K is a large-scale dataset specifically designed for the Video-Next-Event Prediction task, containing 100,000 video clips. Each clip is paired with sequential frames that highlight the context leading up to a specific event. The dataset is structured to facilitate the training of deep learning models, particularly recurrent neural networks (RNNs) and transformers, which can learn temporal dependencies. A sample code snippet using PyTorch might involve loading the dataset, preprocessing the video frames, and training a model to predict the next frame or event based on previous inputs. For example: “`python import torch from torch.utils.data import DataLoader, Dataset class VANSData(Dataset): def __init__(self, video_paths, labels): self.video_paths = video_paths self.labels = labels def __len__(self): return len(self.video_paths) def __getitem__(self, idx): video = load_video(self.video_paths[idx]) # Function to load video label = self.labels[idx] return video, label # Example usage train_dataset = VANSData(video_paths, labels) dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True) “`Academic Context
VANS-Data-100K contributes to the ongoing research in video understanding and event prediction, fields that are crucial for advancing artificial intelligence in visual contexts. The dataset is built upon principles from computer vision and machine learning, particularly focusing on temporal dynamics and sequential modeling. Key papers in this area include ‘Video Prediction via a Hierarchical Recurrent Neural Network’ (Chung et al., 2015) and ‘Temporal Segment Networks for Action Recognition in Videos’ (Wang et al., 2016), which explore methodologies for understanding and predicting actions in video sequences. The dataset’s structure allows for benchmarking various prediction algorithms, leading to advancements in model architectures and performance metrics.Code Examples
Example 1:
import torch
from torch.utils.data import DataLoader, Dataset
class VANSData(Dataset):
def __init__(self, video_paths, labels):
self.video_paths = video_paths
self.labels = labels
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
video = load_video(self.video_paths[idx]) # Function to load video
label = self.labels[idx]
return video, label
# Example usage
train_dataset = VANSData(video_paths, labels)
dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)Example 2:
def __init__(self, video_paths, labels):
self.video_paths = video_paths
self.labels = labelsExample 3:
def __len__(self):
return len(self.video_paths)Example 4:
def __getitem__(self, idx):
video = load_video(self.video_paths[idx]) # Function to load video
label = self.labels[idx]
return video, labelExample 5:
import torch
from torch.utils.data import DataLoader, Dataset
class VANSData(Dataset):
def __init__(self, video_paths, labels):Example 6:
from torch.utils.data import DataLoader, Dataset
class VANSData(Dataset):
def __init__(self, video_paths, labels):
self.video_paths = video_pathsExample 7:
class VANSData(Dataset):
def __init__(self, video_paths, labels):
self.video_paths = video_paths
self.labels = labels
Example 8:
def __init__(self, video_paths, labels):
self.video_paths = video_paths
self.labels = labels
def __len__(self):Example 9:
def __len__(self):
return len(self.video_paths)
def __getitem__(self, idx):
video = load_video(self.video_paths[idx]) # Function to load videoExample 10:
def __getitem__(self, idx):
video = load_video(self.video_paths[idx]) # Function to load video
label = self.labels[idx]
return video, label
View Source: https://arxiv.org/abs/2511.16669v1