Beginner Explanation
Imagine you have a box of crayons and a set of colored pencils. Each crayon color is like a different type of data, like pictures or sounds. Cross-Modal Similarity-Aware Patch Mining is like finding crayons and colored pencils that match in color. For example, if you have a red crayon and you find a red colored pencil, you’re noticing that they are similar, even though they are different types of drawing tools. This technique helps computers understand and connect different kinds of information, like matching a photo with a song that has a similar mood.Technical Explanation
Cross-Modal Similarity-Aware Patch Mining involves extracting ‘patches’ or segments from datasets across different modalities (e.g., images, text, audio) and evaluating their similarities. This can be achieved using techniques like deep learning models that project data into a shared embedding space. For example, using a Siamese network, we can train the model to learn representations of different modalities. Here’s a simple code snippet using PyTorch: “`python import torch import torch.nn as nn class SiameseNetwork(nn.Module): def __init__(self): super(SiameseNetwork, self).__init__() self.fc = nn.Sequential( nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32) ) def forward(self, input): return self.fc(input) # Example of patch mining across modalities image_patch = torch.rand(128) text_patch = torch.rand(128) model = SiameseNetwork() image_embedding = model(image_patch) text_embedding = model(text_patch) similarity = nn.functional.cosine_similarity(image_embedding, text_embedding) “`Academic Context
Cross-Modal Similarity-Aware Patch Mining is grounded in the fields of multimodal learning and representation learning. The foundational theory revolves around the idea of embedding different data modalities into a common space where similarities can be measured. Key papers in this area include ‘Deep Multimodal Learning: A Survey’ by Baltrusaitis et al. (2019), which discusses various approaches for integrating multimodal data, and ‘Learning Joint Embeddings of Text and Images’ by Kiros et al. (2014), which introduces techniques for aligning different data types. The mathematical framework often involves distance metrics in vector spaces, such as cosine similarity, to quantify the relationships between data patches.Code Examples
Example 1:
import torch
import torch.nn as nn
class SiameseNetwork(nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()
self.fc = nn.Sequential(
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 32)
)
def forward(self, input):
return self.fc(input)
# Example of patch mining across modalities
image_patch = torch.rand(128)
text_patch = torch.rand(128)
model = SiameseNetwork()
image_embedding = model(image_patch)
text_embedding = model(text_patch)
similarity = nn.functional.cosine_similarity(image_embedding, text_embedding)Example 2:
def __init__(self):
super(SiameseNetwork, self).__init__()
self.fc = nn.Sequential(
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 32)
)Example 3:
def forward(self, input):
return self.fc(input)Example 4:
import torch
import torch.nn as nn
class SiameseNetwork(nn.Module):
def __init__(self):Example 5:
import torch.nn as nn
class SiameseNetwork(nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()Example 6:
class SiameseNetwork(nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()
self.fc = nn.Sequential(
nn.Linear(128, 64),Example 7:
def __init__(self):
super(SiameseNetwork, self).__init__()
self.fc = nn.Sequential(
nn.Linear(128, 64),
nn.ReLU(),Example 8:
def forward(self, input):
return self.fc(input)
# Example of patch mining across modalities
image_patch = torch.rand(128)View Source: https://arxiv.org/abs/2511.16635v1