Beginner Explanation
Imagine you have a video of a treasure hunt. VSI-Super-Recall is like a game where we ask a computer to remember where all the treasures were hidden in the video. Just like you might try to recall where you saw the treasure by watching the video again, this benchmark tests how well a computer can remember and find that information from the video. The better it does, the better it is at understanding and recalling what happened in the video, just like a good friend who can remember all the fun details of the hunt!Technical Explanation
VSI-Super-Recall is a benchmark that evaluates how well machine learning models can extract and recall spatial information from video data. It typically involves using datasets with annotated spatial features, where models are trained to predict the locations of objects or events based on video frames. For instance, a model might utilize convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) for temporal analysis. An example implementation in Python using PyTorch could involve loading a video dataset, applying transformations, and training a model to predict spatial locations. The evaluation metrics often include recall scores, which measure how many relevant instances were retrieved over the total instances available. Code snippet: “`python import torch import torchvision.transforms as transforms from torch.utils.data import DataLoader # Load dataset and define transformations transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()]) dataset = VideoDataset(root=’path/to/videos’, transform=transform) dataloader = DataLoader(dataset, batch_size=32, shuffle=True) # Define model, loss function, and optimizer model = SpatialRecallModel() criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # Training loop for epoch in range(num_epochs): for videos, labels in dataloader: optimizer.zero_grad() outputs = model(videos) loss = criterion(outputs, labels) loss.backward() optimizer.step() “`Academic Context
VSI-Super-Recall is rooted in the fields of computer vision and machine learning, particularly focusing on video understanding and spatial reasoning. The benchmark aims to address challenges in recalling spatial information from dynamic visual content, which is essential for applications like autonomous navigation and surveillance. Foundational work in this area includes research on spatio-temporal reasoning and memory-augmented neural networks, which enhance model capabilities in recalling and utilizing spatial information. Key papers include ‘Memory Networks’ by Weston et al. (2015), which introduced architectures that can store and recall information effectively, and ‘3D Convolutional Networks for Video Recognition’ by Tran et al. (2015), which laid the groundwork for understanding spatio-temporal features in video data.Code Examples
Example 1:
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
# Load dataset and define transformations
transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
dataset = VideoDataset(root='path/to/videos', transform=transform)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# Define model, loss function, and optimizer
model = SpatialRecallModel()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Training loop
for epoch in range(num_epochs):
for videos, labels in dataloader:
optimizer.zero_grad()
outputs = model(videos)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()Example 2:
for videos, labels in dataloader:
optimizer.zero_grad()
outputs = model(videos)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()Example 3:
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
# Load dataset and define transformationsExample 4:
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
# Load dataset and define transformations
transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])Example 5:
from torch.utils.data import DataLoader
# Load dataset and define transformations
transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
dataset = VideoDataset(root='path/to/videos', transform=transform)View Source: https://arxiv.org/abs/2511.16655v1